{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6b299ee9",
   "metadata": {},
   "source": [
    "# Classification forecasting for categorical time series\n",
    "\n",
    "Recursive multi-step forecasting is a technique used to predict future values in a time series by using previous predictions as inputs for subsequent forecasts.\n",
    "\n",
    "In the context of categorical time series, where the values belong to discrete categories rather than continuous values, the model is trained to predict the probability distribution over the possible categories for each future time step. The most likely category for the current time step is then used as input for predicting the next time step, and this process continues recursively.\n",
    "\n",
    "Although the process is conceptually very similar to that used for forecasting continuous time series, many challenges arise due the need to encode and decode categorical variables, as well as allowing the underlying machine learning model to handle categorical data effectively.\n",
    "\n",
    "**Skforecast** provides the class [`ForecasterRecursiveClassifier`](https://skforecast.org/0.18.0/api/forecasterrecursive#forecasterrecursiveclassifier) to facilitate recursive multi-step forecasting for categorical time series, automatically managing the encoding and decoding of categorical variables, and integrating seamlessly with various machine learning models that support classification tasks."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d1ae70e1-1354-4aa6-9dc4-73d3c7b016c5",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5c54bcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.calibration import CalibratedClassifierCV, CalibrationDisplay, calibration_curve\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.preprocessing import RollingFeaturesClassification\n",
    "from skforecast.recursive import ForecasterRecursiveClassifier\n",
    "from skforecast.model_selection import (\n",
    "    TimeSeriesFold, OneStepAheadFold, backtesting_forecaster, bayesian_search_forecaster\n",
    ")\n",
    "from skforecast.plot import set_dark_theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc329150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────── <span style=\"font-weight: bold\">vic_electricity_classification</span> ────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                   │\n",
       "│ Hourly electricity demand for Victoria, Australia classified into three        │\n",
       "│ categories: 'low', 'medium' and 'high' according to the 20th and 80th          │\n",
       "│ percentiles. The dataset also includes temperature, holiday indicator and hour │\n",
       "│ of the day as features.                                                        │\n",
       "│                                                                                │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                        │\n",
       "│ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse       │\n",
       "│ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/,                    │\n",
       "│ https://github.com/tidyverts/tsibbledata/.                                     │\n",
       "│ https://tsibbledata.tidyverts.org/reference/vic_elec.html                      │\n",
       "│                                                                                │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                           │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                       │\n",
       "│ datasets/main/data/vic_electricity_classification.csv                          │\n",
       "│                                                                                │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 8736 rows x 5 columns                                                   │\n",
       "╰────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────── \u001b[1mvic_electricity_classification\u001b[0m ────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                   │\n",
       "│ Hourly electricity demand for Victoria, Australia classified into three        │\n",
       "│ categories: 'low', 'medium' and 'high' according to the 20th and 80th          │\n",
       "│ percentiles. The dataset also includes temperature, holiday indicator and hour │\n",
       "│ of the day as features.                                                        │\n",
       "│                                                                                │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                        │\n",
       "│ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse       │\n",
       "│ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/,                    │\n",
       "│ https://github.com/tidyverts/tsibbledata/.                                     │\n",
       "│ https://tsibbledata.tidyverts.org/reference/vic_elec.html                      │\n",
       "│                                                                                │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                           │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                       │\n",
       "│ datasets/main/data/vic_electricity_classification.csv                          │\n",
       "│                                                                                │\n",
       "│ \u001b[1mShape:\u001b[0m 8736 rows x 5 columns                                                   │\n",
       "╰────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Demand</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Hour_of_day</th>\n",
       "      <th>Demand_raw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>low</td>\n",
       "      <td>24.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3730.065980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 02:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>25.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3858.473927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 03:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>25.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3851.415438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 04:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>23.65</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3838.972926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 05:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>20.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3860.540970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 20:00:00</th>\n",
       "      <td>low</td>\n",
       "      <td>12.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>3527.232855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 21:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>12.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>3846.439766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 22:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>3961.529675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 23:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>16.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>4059.288011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31 00:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>17.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4071.800790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8736 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Demand  Temperature  Holiday  Hour_of_day   Demand_raw\n",
       "Time                                                                       \n",
       "2014-01-01 01:00:00     low        24.80      1.0            1  3730.065980\n",
       "2014-01-01 02:00:00  medium        25.90      1.0            2  3858.473927\n",
       "2014-01-01 03:00:00  medium        25.30      1.0            3  3851.415438\n",
       "2014-01-01 04:00:00  medium        23.65      1.0            4  3838.972926\n",
       "2014-01-01 05:00:00  medium        20.70      1.0            5  3860.540970\n",
       "...                     ...          ...      ...          ...          ...\n",
       "2014-12-30 20:00:00     low        12.10      0.0           20  3527.232855\n",
       "2014-12-30 21:00:00  medium        12.30      0.0           21  3846.439766\n",
       "2014-12-30 22:00:00  medium        14.05      0.0           22  3961.529675\n",
       "2014-12-30 23:00:00  medium        16.60      0.0           23  4059.288011\n",
       "2014-12-31 00:00:00  medium        17.70      0.0            0  4071.800790\n",
       "\n",
       "[8736 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data download\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name='vic_electricity_classification')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "093cb50d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of classes:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Demand\n",
       "medium    0.6\n",
       "low       0.2\n",
       "high      0.2\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribution of classes in the series\n",
    "# ==============================================================================\n",
    "print(\"Distribution of classes:\")\n",
    "data['Demand'].value_counts(normalize=True).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5dda5605",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates : 2014-01-01 01:00:00 --- 2014-10-31 23:00:00  (n=7295)\n",
      "Test dates  : 2014-11-01 00:00:00 --- 2014-12-31 00:00:00  (n=1441)\n"
     ]
    }
   ],
   "source": [
    "# Split train-test\n",
    "# ==============================================================================\n",
    "end_train = '2014-10-31 23:59:00'\n",
    "data_train = data[:end_train].copy()\n",
    "data_test  = data[end_train:].copy()\n",
    "\n",
    "print(\n",
    "    f\"Train dates : {data_train.index.min()} --- \"\n",
    "    f\"{data_train.index.max()}  (n={len(data_train)})\"\n",
    ")\n",
    "print(\n",
    "    f\"Test dates  : {data_test.index.min()} --- \"\n",
    "    f\"{data_test.index.max()}  (n={len(data_test)})\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c055f932",
   "metadata": {},
   "source": [
    "## Plotting categorical time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98b2faa",
   "metadata": {},
   "source": [
    "Visualizing categorical time series can be challenging due to the discrete nature of the data. Most of the standard plotting libraries require numerical inputs to create plots which means that categorical data needs to be encoded into numerical format for visualization purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba00995f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding map: {'low': 0, 'medium': 1, 'high': 2}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Demand</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Hour_of_day</th>\n",
       "      <th>Demand_raw</th>\n",
       "      <th>encoded_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>low</td>\n",
       "      <td>24.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3730.065980</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 02:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>25.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3858.473927</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 03:00:00</th>\n",
       "      <td>medium</td>\n",
       "      <td>25.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3851.415438</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Demand  Temperature  Holiday  Hour_of_day   Demand_raw  \\\n",
       "Time                                                                          \n",
       "2014-01-01 01:00:00     low         24.8      1.0            1  3730.065980   \n",
       "2014-01-01 02:00:00  medium         25.9      1.0            2  3858.473927   \n",
       "2014-01-01 03:00:00  medium         25.3      1.0            3  3851.415438   \n",
       "\n",
       "                     encoded_value  \n",
       "Time                                \n",
       "2014-01-01 01:00:00            0.0  \n",
       "2014-01-01 02:00:00            1.0  \n",
       "2014-01-01 03:00:00            1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing\n",
    "# ==============================================================================\n",
    "# Encode categorical variable as numerical for plotting\n",
    "encode_mapping = {'low': 0, 'medium': 1, 'high': 2}\n",
    "print(f\"Encoding map: {encode_mapping}\")\n",
    "data['encoded_value'] = data['Demand'].map(encode_mapping).astype(float)\n",
    "data_train['encoded_value'] = data_train['Demand'].map(encode_mapping).astype(float)\n",
    "data_test['encoded_value'] = data_test['Demand'].map(encode_mapping).astype(float)\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "834df1cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot: line plot\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "data_train['encoded_value'].plot(ax=ax, label='train')\n",
    "data_test['encoded_value'].plot(ax=ax, label='test')\n",
    "y_ticks = list(encode_mapping.values())\n",
    "y_labels = list(encode_mapping.keys())\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_title(\"Categorical Time Series (States)\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5ee2bfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zV2SOy83b91WZjHwuZM6FJFtF38unyotij1g4vSxpkrI3+UnMMZr4PE3d1ariiz1yZxIZFfXyvqDX3ifnwngXx09EycOkgOgdkpptqd2Wshwpi0iszzp/rE6AlNCJmj5uWEslBUkREPDin3LXARPVBOC3Pj7oxePlPAoJkZrn1/3jd872omQqPmmICv8EGgjxe8/fxDSxNDGJlvQeSoPv8vXbaNZl+CujHhLL1CInrRMyodhEJjtLQpDQKk7lSxVWScF/JROJpeErE4slKTBNMF62PnGlQ28iIzUy4V0SDhkFkhIyacy+TaliBVVCICtudew7Lk49vZQCfd6lVZL2w/SekBOFySpAiSUlLPIjCZissNSoblW1OMCKOeNRq0WfN44ayD6fOX8VVcqXQM3KZd6YFEjZnLxfJRmN32suieDIfp3RrEFNVc71SbO6L26PVTokAl4e49ffL8Dilf+/ulNixKuwM0v/5fiJKHk4p4DoHZJVcaT3UxpAUiLxJlaW/5+TyxljxdZ/Dr/yOGmEJMTUsJJa8fhOnb+iLqXhlhiyeouclVdWYkmo5y2heQEy8VSYlo6Mz3T7+QTKBZJCSiGkJ12ORXqq30Qa3FKSIiMt8RMCaaTlccuJ1CDJiZTKiNjLyb7pdZalOt8FSYik3Kth3aooW6KQSlJlNSl5jd+VwJexlQa9ifFlb68hgfejx8vjlpGF2AmBkH2MXyonTI9LaHtyLgs//0CUK1k4TmlYUpIKKZuT5UIlCZdSpCrlEv6cxbZi44tyqV6dP1ZzN17HVNIkZy2PPzqzbstu9X6WEiLZd0lUnz57rpbsjO3U+avqslK5Vz97WqD14ydKC0wKiN4hWXVFeoClxOD3H8eqHtKEyERDWVUo/lKi1SrEbZjIsLn0LifEtBKMzD9IaKKhrKXetU0TVbueEOmZNjWyZUKzrNXukMUOA3u2ifM4mfAnyyjGJ/XY0tCUxnr8yX5yXZIZKZWQBup/Ib23UgMutfZjBneL0xA1rT5kKmWSdfFNSUzsZEkeM3XMF2rCdkqTeK2aNxHZsjqohl7skRrTqjzxEylZt13Kct6VJWu3qffgwumjXk4wfvsypLH16dJClcElpFKZYmr/ZanXU/++SD7f9n40vb9lxaz4ydPkUb0TfB7f5/4xy1ImlDD8uvJP9Z6QFWgSaqDLhN7YifnrktfsL0fHEjNfRc7sfPD4OXUujAXTRr5SGiexlhO/yZwWKVub8vOyV7YhixDIfBsZqejRoZl6/o3b96nzksQm5Uwy76Hxy9GMhBQpmPeViewZhdaPnygtsHyI6B37cfFadRKlwZ+1w/YVM1VJkZxsJyg4VJXnVC5fXNVCn421fr8stSnreM+fOlItoyj1+IUL5lVrjcvkUJmcGd/BY+fUnIHFM0Zh18FTCA0Nw/2HPqp3UhoYUlYhZRHLfhqn9kHWm5eGj6tLdrUevvRal67XOaYxJMuQVq9YCn3/11qt8S6rCslZaz+qXwO7D55UpRZGY9y6hAFfz1QNYKkXl0REenAL5HVX50KQ8qL+X818ZbWg5Pjy23koXCBvTK28zBWQRmluNxe8X7Ucug6cqHrDZe6DnCNC1uvfuXq2GjGQ+RK1qpRRa9hfuHITJV5OkP6vcr88UZ2pFlp6m2XdfYmtkDKVEd/8HOdv1v65G727tMD4YT1QrWJJlbjJpG9Z013OI5HQ65wcf+44hPFDe6jXWnqhpcwmKaRR+/WgbmppTZn78PjJM5VYFc6fRy0nK43fcdMWxcwbEXLCOlmpSFYnkucLDApRPfKykpG81yU5lGRx85Ip6vfs2RzV+Qlu3n6Q4HyRU+euqJWlpOEsk7NN81DkhGry3pq5cJVKwOQ98WHtSuoEWNIQz57VQcVUSpu+/2mZOgaxeOZo9Rk8/e/VF8mZ7sVImoxUyLk03rbMqJA5CD2HfIsF00apFb6ObF2EXQdOqM+dnGRLkn0Zrbr74DG69J+Q4FwcsWbzbrU858gvOqvraze/urqX6DtqGtYsnIQZ4weoE/2dPn8N/gGBakKuzEOQn6adh76yxGpGofXjJ0ptTAqIUsDMBauwZedBtfa61JbLai2ZMlnC1y9ANc7n/LpeNd5jl3x80nM0hvfthHo1K8LCoFdLgUrDXpYJTaixKKUM7q7OaN6gFj7v0lL1gksNuWm7sk1Zk13mK0ijU/bBGG1Uk6Gl9EeW9Is9sVEmjDbrOgwj5YzG1SuoxpKc0VjW9JfGmSQFprkHsUt7GnccjAE926oyFTk5mGxTliSctWC1Wh3mXZAJmc26DEPPTs1VItSpZUNEGaPU0pCrNu3EtZsvGn5iyPgfcefBY/W4rm2b4Jnvc9VbL7Xw0nP+rsiEZdMkV0mspAEsjfy5SzaokqGETvoljegW3Ubiy/5d1GiGNCJltGX05HnYf+zsO0sKZORHerVlMu2aLbsSXKL1TQaNma3Oai0JgPTuy5wSGaGRRre8tkvX/KVGimKTBEwSBVmdqEfH5mqkQkZuJCmQxrTMbxner7NaQ79b+4/UtuQ9PHvhGuzdEDd5Mr3m8v4f/Fl7NUHcVNa2fttelRRI4vu/QZPUcp5yv7zHZeL8U19/3HvwGFPmLI9TujV59hIVbzmzcr0aFRAaHo4HD73xzaxf1VnD4/fUv47sV9vPvlLJcusmddRqYTLxWRKOG5731BLCMq/jTSMPMg9D3i8yt0E+p+evJFxiJ8lSw/aD0K19UzSpVw0tG9dWCZkkG3KiQEmQ5O8zKq0fP1Fq0yFH+QwwJYmIUtKIfp3VyaLa9xnz2rMbU/oiZ4CuUq44an7c+60r5RAREXFOARG9smJQ/LpdWdFHRgDinwiN0ic5t4Mqszp8hgkBERElCsuHiCjGX8tnqHXkr9y8i5CQUFWbLaUWer0OI76Zk3on/qJk+fSTRmryrcxPkRVups1bnta7REREZoLlQ0QUQyZHN6xTRZ0wzc7GWtU9y9KA85ZuUBN5KX07tm0RcuXIjjsPHmHGvJVxzpBLRET0JkwKiIiIiIg0jnMKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaVy6Tgocra1h0OnSejfMhsSKMUsaxix5GLekY8ySjjFLHsYt6RizpGPMkic9xytdJwVONjYw6NP1LqYrEivGLGkYs+Rh3JKOMUs6xix5GLekY8ySjjFLnvQcr/S7Z0RERERElCrS9RmNSwyajPDISERHp9tdTFd0Oh2sLCwYsyRgzJKHcUs6xizpGLPkYdwyZsx+3fsV0l3MDAaER0Wl25ilRzqdDtXPp88+eQukYxZuHul7B9MpxizpGLPkYdySjjFLOsYseRi3jBWzKlmQzkgiEJnWO2GGopFepc9UhYiIiIiIUg2TAiIiIiIijWNSQERERESkcem5fA6RDzzT9aSf9MYcJkqlN4xZ8jBuSceYJR1jljyMW8aM2dEApCucaJz8uKVX6Xr1IY9s2fDAz0+94ejt5MPp5ujImCUBY5Y8jFvSMWZJx5glD+OWdIxZ0jFmyWNKpNIjlg8REREREWkckwIiIiIiIo1jUkBEREREpHHpeqIxERERkSgzbWWKP4dDrN/PDm2PtHZs2yIsXL4Zi5ZvRnoTXCkcgHeqPZ/FEUOqPZdWMSkgIiIi+g+8zm554/3T563A9HlJT2oadRyMkJCw/7Bn2pRSr4dp290GTcL2PUeR0TApICIiIvoPStfrHPN7swY1MaxPR9T8uHfMbUHBoXEebzDoERVlfOt2n/n6v+M91Yakvh70AucUEBEREf0HPk/9Yn4CAoMRjeiY6wU93HHjyFrUqV4e21fMxO0TG1CpbDHkdc+JX2d+iXO7luL64TXYtnwGalYu/Ur5UI+OzeL0UndoUR+LZ4zGzSPrcHDzfNSvXSkNjjh9e9PrIT8fN6yFfRvm4Nax9di/cS66tGkc87eWFhaYNPIznNm5RN1/fNti9OvWOub1EL/M/FK9FqbrGQVHCoiIiIhS2Oj+XTBx5i+4c/8RnvsHwjVnduw6eArf/fQ7wiMi0LppXfw2+2vU+rgPHjzyee12Bn/WDt/M+g0TZ/6Kbu2b4qfJQ1CpUXf4+Qem6vGYqxaNa2Nonw748rv5uHDlFkoUyY+pY/ohOCQUa7fsRvcOH6F+7cr4bPj36nVwc3FWr5WpnOvCnuUYOGYW9hw6hSjj20d7zAmTAiIiIqIUNm3ucuw/ejbmujTiL127HXN96pzlaFS3qur5/3X11tduZ/XmXfhj+371+7c/LEWPDs1QpkQh7D18OoWPIGMY2rsDJsz4BX/tPqKu3/N6jEL5c6Nz64YqKXDL6Yxbd71w/Mwldf+Dhz6vlHP5BwSpEYeMhkkBERERUQo7d+l6nOs21plVA7VezQrI4ewEC4MBmTNZwS2X8xu3c/n6/ycSIaFhqoGaPWvsdZPodawzZ4JHHldMH9tfjQ6YGAwGBAQGqd/XbN6FlfMm4MCmedh76DT+OXAC+46cgRYwKSAiIiJKYcHxVhEaM7gbalUpo3qtb997iNCwcCycNhKWlm9umkVGRsW5LvXyej2niCaGrY21uhw68UecOX8tzn1RLyd+n79yE1Wa9EDd6uVRs0oZzJsyHAeOnkOvYd8ho2NSQERERJTKKpYpqnqlTUtbysiBu2uOtN6tDO3JMz889H6KvG45sXHbvtc+LjAoBJt3HFQ/f+48hJVzJ8DR3k6VfMn8j4yahDEpICIiIkplnne90LheVezcfxzR0dEY/nkn6HUZs7GZnkyfuwITh/dSqxLJZGErK0uULl4QDlnssGDZJvTq1BzeT3zVJGSj0YiPPqyBxz7P8DzgRXnRPS9v1KhcGifOXkJ4eETM7RkBkwIiIiJK91LyDMNWBgPcHB3xwM8P4VFxy3NSyrjpizFjXH9s/m0qnvn54+ff1sHOzgbmwua4VarH7F1YsXGHmovRp0sLfDXof2rVoSvX72Dh8k3q/sDgEHzetaWaeyAlRecuXkfnfuNV4iYmTF+MsUN7oGOL+njk8xSVG/dARqFDjvIvjjId8siWzezebGkpLb7UzB1jljyMW9IxZknHmCUP45Z0jFnSMWbJj1t6jRfHqYiIiIiINI5JARERERGRxjEpICIiIiLSOCYFREREREQax6SAiIiIiEjjmBQQEREREWkckwIiIiIiIo1jUkBEREREpHFMCoiIiIiINI5JARERERGRxjEpICIiIiLSOCYFREREREQax6SAiIiIiEjjmBQQEREREWkckwIiIiIiIo1jUkBEREREpHFMCoiIiIiINI5JARERERGRxjEpICIiIiLSOCYFREREREQax6SAiIiIiEjjmBQQEREREWmcBdK5OXProMV763DbWBNOutvYHD4PZS1+xb+RnVDKYjk89LsRjixw1l2GT3RRdfk0uhDcjj7F3byF4bH9KTybOiGfy5/YGfEdqlnMgM+jSiiwywu6Io+A4x5AtkAg1BJwCAGe2QL6aKCAN1DzOq4amwCIRqbIYOTaFoX79W2R12YPPI3vI79+F26FNUCBvx7Bt7wO9llu4ZBdXxTUb8fWiB/QwHI4vHxrotKVTYh6nBWZHH2AwEx4UDEr7A9bI6rIM9idsMGNOrmR7/INeJbKD4/dj5HZ/Q6Mz+1ws4kzPPyP4Y6xJvIdvw//fJngdCEScAxGsC4rgnMZkP1yoLp+q1gheF99iA4/A4e6Z0KBmoGw32cHQ7kbiD5SAM/qRcDpGHChRUEUsfoDy8L+RAHDDjhF38F7gfvgE1YSjvefI9DFGsgcDn3259DJcSMA940VkU1/A6ciuyMaBuTUn0PZa3/D07UU8h++i1sf5EJ+y524ZayLfPp9uGusDjf9SXhGvQ8X/UWcjvwfChp2wPKBFayCI2C45QjL7N6wup0Jjxvq4JzlDB4ay8JdfwwnInujnMUvOBb5Bcr5r8QV64Yoe+kf3LWpgNx3ruNB/cxw0V/AU2NB2OseIDjQFYYAAyJyRuC2sRYuR32MwoatKGexGN7GYnDVn8Z9YxXk1e+HZ1Rd5D96G7dKFEa+gBM4m6spQm8fQZfhjtg63wru+Z3gpj+BgGhX6GB8+RMFKwQiONoZDvq7uBbVGLn1R3AocigqWczF/cjKKL/rCG4VKo4C++7Ct4wFHAJ9cKe6C9z1x/HQWAbZ9VfV8WXTXVcxLGWxElEhtogKsoP1HT2eu9nB2esRvPK7I6/xKG5lro73LLfhRHQvlM20GNvCf0Bew35khj8Kh/+N61Z14RF+GPesyiN3+Ck8zZQPVroArApbjxqWU2GBUGTWPcepyB4ooN+BJ9FF4KHfgwLPjuK+XWnk1R/AJX1zFA/egoOZB8DV8oR6rapbTseysK2oajELNsH+cPJ/hKBMWWH3LACReYJh8LJG5rAQ+Fm54sEeoN1fRhxY6I3Measif+R+PPKvDI9tT/CwsiNczz/Gw/JOyPXsOvycssE23Bd+UR6wtbuHh3nyqFg8NpZAdlyH3tMRRoMedkYf+NjmQ/bIm/DMUhnv6f7GVasGKJp5HY5EDkJFi7n4O2Iqyhp+RUC0GzyeH8M9hzLI/egSnrjkhI1/MELsbGDzLBRGu0j4G3LBNrMXfIxF4ay/DF9jAeR/fhTXdfVR5MFBeOYqjfzeJ/EwayG4+lzHQ8dCyBF1GVfdqyGv4QAuRbZEcYv1eH6mGmwM3gi/7w5dLj9YZApAiENm2Ec+RqDRBdmD7uJmnrLIa78TpyO7obzFYlyLaoRi/tvh6d8QHt6n4W1XEI+vP0SrJcChbpmRr5gVLO2fISRPNBBkiSBbB+hD9XDa6IDQir7IekSPJzV0yLE/Av61g+C42xq+dcLh5PsYN0sVRn7dHlyybIbCz3fh/rWPkNvrCoIcbGEdFojw6CyIyhYG/X0HRBQIQOb7OviWtIJj1ku4F1UNOSMuIjrQGo477eBbIwJZj0dDV/Maoi+5IqRIGKy9ohFqbQvLxxbwaRIOu+fP4WlXBfmenoExwBaW0aHQP7VGpHM4rA7lREjZANget0Vg1WDYH7XCo9YRyBl4FQ+dCsH1yTX4BhRF1lOR0BX3QvSNnAh1j4LFE0sE29rBIdgb0EUjwNEBkZW8EBVgD2SKgOVTPWx2u+Bq7ptoNt8Rp74IQIn7uYDqN4CT+YAqNxH9bx48aBONXD438ChnXrj4eSLwaX61f6E3CsLC0ReI0iMyayQyG/wQFWqDzGccYKxwD5a5HiLcYINMYSHQB1kAZ/IARR8i+nQ+PCtjCadLYfArYA37B0EIzGUNa0sf+JXWwzIsAo91xVH04f4X/zvy+wCXXYHS94AT+YCqN4FLL65Hh1jhWcEsyGq4gRuWddV3ufftWnC5/AT+ea3h5P30xf8cQzTg8hywigIso+Bn7wy9gz+8jSWQJfKx+p7IcdUXFrm8AT8bPM2aE1kDH+BZFjdkjfLEM2MB2Bu9cDJvS7joz+HfqE7IdGcV+o4A9vW1QdHCwbCwDoD+SWZY57gL+FsjxMEKmaKDcNuhPHQOQXhkLIOg6BzIob+A4hF/4JZlLXg8P447juXh7nceDx2KwO32bQRb2QO24bjpUA1ZdF7YEzEWNQzT4Op3GQFR7si20wp+NcOQI/AG/DPlhOONYPhWApyiPXEhy0coZrUOf4QvVp8v+b531N1Vn08bnQ+Co7PBQhcGfaQRuiArZHLwxtPognhkLA0jDPCLzgcP/T4UNmxW3/PFA7fioU1xuAZdxMMsReDifxN37MvCxXAeuyPGobhhPf4Kn6G+1/IYjyHMmAV2Vl7wi/SAc/RVPEduZNNfxx19VXjo92Lz9Y8xZORzTJ7TDR8W3As9jCh6+yD8M+WAnZMngn3zQOf6DEG++eCQ9TKiw6zgl8kNlgjBA2MF2OiewhhtgfyGXbhvrIy84Ufx2KI4ch4Iwq0qHshvvR2eQQ2Q94InrlYohgKGnTgUMUx9t8t3tsQiAjaq3SPbDEZW2OvuIzA6J7I/8MYNx+qwt/VUn2FH/W0YEKG+y49H9kFRwybcMdZAccNaPDSWg7P+Ep49Lw67++F4XtQSIdFZ8U/EJPUdWsSwSX1/ZtNfUzF1Cb8CT9SEx9/P4FcrFE52V3BB1xqFDNuwP2KUenwYHNTrJa+Vo94TgWHuyBwWjIuHc6HLQmDvAj/k8MgNW503HhtLwkF3Dz7hJVD21H54VnFXMbll/AD59f8k2E5wNxzHpeA2KHV1D6IeZ0dIPsAgzTK3aGS5Fg3fcno4Hw/Fs3IG5HQ6iitRzVX8DkcOUcd0N6o6Chu24I6xlvr/LK9HnHZCVHYYIqMQAVtEhVtDZxes4izfS94Fs8P19FPcr5kVea32wtNY5+V+yv7uwqnInihjsRTHIvqhfMBy+ESUQFYfHwTktYKVpR+CrRzgF+2BbRGzYA1f9Rpm111R7QcnvaeKY2R0ZtUGOhAxHNUsZ2HjtTbo3Oso0iOOFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcTrkKB+d1jtBRERERERphyMFREREREQax6SAiIiIiEjjmBQQEREREWkckwIiIiIiIo1jUkBEREREpHFMCoiIiIiINM4iKQ/u1601GterhoL53BAaFo6T565g0qzfcPPOg5jHZLKyxNgh3dGsQU31+97DZzBq8lw8eeYX85iJw3uhYpmiKFwwL2543sOHbQe89jnz5c6FHatmIcpoRNGa7d+6j13bNkafLi3hnM0Jl6554qvv5+Pshesx93ds1QAtGtVGySIFkMXOBkVqtoN/QBBSirnHzNHeDkP7dEDtqmXhmtMZz3z9sX3PUUyZswwBgcFIKeYeN/H9V31Rs3JpuDhnRXBwKE6eu4xJs5fgxu37SAkZIWbrFk1GtQol4/zN0rV/YeSkOUgJGSFmonypwhjRrzPKlSyMqCgjLl69hQ6fj1XHlBLMPW7urjlwfNviBP+u17Dv8OfOQ3jXzD1mwjmbI74e1A21qpSBna01bt5+gNmL1mDbrsNICek9ZpXLFcfnXVqiZNECyJkjG7oNmqT+P8Y3rE9HdGhZH/ZZbHHy7GWMnDwHnncfIqVkhLg1qlsVn37SSD0mq6M9PmzbHxeveiKlmHvMLCwMGNG3E+rWqIC87jlVu/bAsXOY/MMSPPZ5ljIjBVXLl8Bvq7ei6afD0K7312onVs6dAOvMmWIeM25oD3xYqxI+G/Y9WnYfpRpEi2eMemVbqzbtxOa/D7zx+WT7c74bhmNnLiVq/5rVr4GxQ3pgxvyVaNB+oPpSWzFnArI5OcQ8RvZ176HT+HHxWqQGc4+Z7IuLczZMmPEL6rbuh4FjZuH96uUwfWx/pCRzj5v49/INDBo7G7Vbfq4aaDqdTh2DXp8yA3QZIWZi2frtKF2vc8zPN7N+RUrJCDGThGD5z+Ox/8hZNO40BI07Dsavq7fCaDQipZh73LwePYnzHpOfqXOWIzAoGLsPnkJKMPeYiR++GYwC+dzQdeBE9f9AkoH5U4ajROH80GLMbKwz4+I1T4z+dt5rH9O3ayt069BUdWw07TwUwSGhKq7SqEwpGSFu8pjjZy5h8uwlSA3mHjPrzJlUwjBr4Wo0aDcQPYZ8qz6rv836Cql28rKsTva4sGc5WnQbiWOnL6qe9/N7lqHvqGnY+s+LnoOC+dyx/4+56sNw+vzVOH8/pHd7NKxT5bWZ1JcDuqgG6cHj5zB+WI+3ZlJ//j4N5y5ex5ffzX9xcDodTv79K35d+Sd++nVdnMdWrVAC6xd9m+IjBRkpZiZNP6yOHycNQcGqrVWvZGrICHEr+l4+7Fr7I6o27Yk79x8hpZljzGSkQHq5x05dhLRgjjHbsnQq9h89qxq1acUc4xaf9Nidv3wTQ8b/iNRgjjG7fngNRk6ai/Vb98T83YW9y1XDbcXGHdBazGLzOrslwR7vMzuXYP7vf2De0o3quuzzuV2/Y9CYWdj0loajluNmYhrVS+mRgowUM5PSxd/DX8tnoGLDbnjwyAeJ8Z+6LO3tbNWl3/MAdVmqaEFYWVqqIQsTKZW47+WN8qWLJGnb1SuWQtMPa2D0t3MT9XhLCwv1/LGfOzo6GgeOnVU9aelFRoiZHENgYHCqJQSm5zTnuEkW37b5ByoZkF7K1GCuMWvZ6H31Zbx73U8Y9cWncXpqUpq5xUx6ccuXKoKnz55j85IpOLdrqersqFSmGFKTucUtPulhK1GkAFb+sROpxRxjJiUVUjohZaWSNDRvUBOZM1nh8Mnz0FrMEiOPm4vqTZY4mkjZ7Znz15K8f1qKW3pgnwFiZm9no0aMnwcEpsycgtjkC2H8sJ5qeOfqzbvqthzZnRAWHvFKz7vPMz/kyOaY6G07OWTBrAkD0e/L6QgMCkl0VifDMT5PfePc/uSpn8rm0oOMEDOp7RvYsy2WbfgbqcWc49alTWN8NbArbG2sccPzvhqWjIiMREoz15ht/Guf+pKVGsiihfLhywFd1RCoDIWmNHOMmdSOisG922PizF9w8YonWn9UF6sXfIO6rfumaN2yOcctvvYt6uPazbuq0ZsazDVmnw3/HvO+H45L+1ciIiISIaFh6D54Mm7f0977LDFk/9T+PPVLYP9e3JfSzDFuaU2XAWIm5Wny//OP7fuT9DzJHimYPKo3ihTMgz4jpuBdmzqmn2ocyJBNQiqVLaaGMU0/LRrXhjkw95jJxLKlP47BtVv3MH3eCqQWc47bhm17Ub/dADUEeevOA8yfMiJFa0nNPWbL1/+NfUfO4MqNO9i4bR8GfDVTTf4yNX5TkjnGTK/XxczDWL1pFy5cvYVx0xbh5u37aNf8Q6QGc4xbbNLT3aJRrVQdJTDXmA3/vKOaLNum15do1HEQFiz7A/OmDEeRgnmR0sw1ZmmNcdNezCwsDKqtIclNUhfpSNZIwaSRn+HDWhXRotsoPPR+GnO79xNf1eCRL43Y2ZRzVkd4x8uU36R6pVKoX7syen/aQl3X6QCDwYC7J//A8Ik/qcwndp2WZOHh4RGIjIxSqybElj2bI3yexO39SAvmHjPp6V4xZzyCgkLQffAk9XepwdzjJkPF8iM9tqf/vYrLB1aqVRVkuynF3GMWm6lOU1ZpSMl5GOYas8c+Ly6v3bwX5zEyKuWWyxkpzVzjFluTD6qrErW1f+5GajDXmEli3q39R3i/VV81qiIuXbuNymWLo2vbJim2Qlh6jVliyP6p/cnmGPO7af8uXruFlGaucUtLk8w8ZqaEwC1XDpW8J3U0wiI5AWtYtypa9xiFe16P49wnq62ER0SgRqXSMUuUFcjrpiaKnErCsOxHnw6DIdYKLQ3qVFEz+Jt1GYZH3k/VclEJDVfK89eoVCpm8oVkSbIvv63airRk7jGTEQJZLUH2s+vAb9QQWmow97jFJx9+HXSwSsGRgowWsxJFXqxqEvsf6rtmzjGT/ZV/XFJiFVv+vK7YfShlVtHJCHGLrX2LD7Fj73G13HJKM+eYmeb2xF/VSpZTNI1YaS1mb3P3wWNVCin7Z5okK/9Py5YshKVrtyElmXPc0sokM4+ZKSHwyOOK1j1Hw/flfIgkbSMpD548uo8aZv3fwEkq+5DsV0hPqByIXK7cuBPjhnRXkzMCgoJVkGV99tgzs6XXz9Yms+qVkKHb4oU9Ynq7pN5aerniz6A2RhtjarteZ8Hvf2DWxEE4d+kGzly4hp4dm6tlnFZt+ifmMbLPUhvmkdtVXZdhz6DgEDx46AM//8RPxtBKzOQLzLQs1xdfTlfX5Uc89fVPsWUPzT1uMsFMJuRJKYw0NnK5ZEO//7VGSFgYdh04mQIRM/+YSU+knENk18GT6sus2Hv51BJwR05ewOXrt1MgYuYfMzF3yQYM7d1BLSEpDY9PPqqLAvnc0XPod0gpGSFupuevUq44OvUbj5Rm7jGTSZW37nphyld9MWHmL/D1C1Crq8g5Cz7tP0GTMZP4eOTJFXM9t5uL2rbf88CY1V4WLd+MAT3bwvOul0oShvftpBKFt60co/W4yWR2Ge2UidqmBripgyglRhzMPWYWFgYsnDpSLZogn0dJPEzHII9J7FzGJC1JKssgJUTWrl+zeVeckzs0b1jr5ckdTquTO8R+ERM6QZGo1Li7mmQYX5tm9RK9ZNP/2jZ5cfKV7E5qacOvv1+gvuBiLxM1pHeHNx7Du2TuMTMt3ZqQ1z33u2DucZMvsmljv0CpogXgYG+nJuwdPX0RM+evinMylHfJ3GPm6pJdLXVbuGAe9QXo9fgJtu8+otZdTqkJWeYeMxNJOOXEU44OWVRy8M3M33D8bOLWv9Zy3EZ+0RmtGtdRzyer7aSkjBAzaZSM7t8VlcoWVSWlUhYpS23GXqJUSzF73f/H1Zt3qSVHY5+8TE6cKqUnJ85cUvsnCVZKyQhxk23JhNz4ZD7j9Hkr8a6Ze8zc33BCxlY9RqnOtRQ/TwEREREREZm/lDm1KhERERERmQ0mBUREREREGsekgIiIiIhI45gUEBERERFpHJMCIiIiIiKNY1JARERERKRxTAqIiIiIiDSOSQERERERkcZZpPUOEBFR8sU/82doWDj8ngfgyo072HXgJFZt+gdBwUk/I3SF0kVQu2pZLFy+Gf4BQe94r4mIKL1hUkBElAFM+XkZ7j54DEtLA5yzOaFahZIYP6wHenVujq4DvsHl67eTtL0KpYtiSO8OWL15F5MCIiINYFJARJQB7D50Cv9euhFz/adf1qF6xVJY+sMY/Db7K9Ru8bkaRSAiIkoI5xQQEWVQh078i5kLVyG3qwtaNnlf3Vb0vXyYOWEgjvy5ELeOrcfZf5Zixrj+cHLIEvN3Q3q3x5jB3dTvx7cthtfZLerH3TVHzGNaNn4f21fMxM2j63Bx3wrM/W4YXF2yp8FREhHRu8CRAiKiDGz9n3swun8XNT9gxYYdqFWlDPK6uWD1pn/g/dQXhQvkQaeWDVGoQB407TxU/c22XUeQP68bWjSqjTFTF+KZr7+6/emz5+qyf482GP55R2zZcRArNu5ANicHdGvXFBt++Q712w1guRERkRliUkBElIE99H6K5wGByOeeS11fsmYb5v/+R5zHnP73KuZ+PxyVyhbD8TOX1PyD85dvqqRg+56juO/lHfNYt1zOGNq7A77/eRl+XLw25vZtuw5jx6rZ6NKmcZzbiYjIPLB8iIgogwsKDoWtrbX6Pfa8gkxWlsjqaI9T56+q6yWLFnjrthrXqwa9XqdGCeRvTT8+T/3gedcL1SuWTMEjISKilMKRAiKiDM7WJnNM6Y+jvR0G926P5g1qqlWKYrO3s33rtjzyuEKv1+PwlgUJ3h8RGfWO9pqIiFITkwIiogwsV45scMhiB897Z9X1+VNGqOVG5y7dgAtXbiE4JBQ6nQ4r505QIwBvo9fpYDQa0bHvOHWZ0KgEERGZHyYFREQZWKumddTlvsOn4ZDFFjWrlMHUOcsxc8GqmMd45Hkx3yC26OjoBLd35/5DNVJw78Fj3LrrlYJ7TkREqYlzCoiIMig5T8Ggnu1w5/4jbNi2F1Eve/Z18QYEenZs/srfygiCkEQiNlmZKDIySpUgJST20qZERGQ+OFJARJQB1K1eHgXzucPCwgDnrI6oXqmUWn70/kMfdB0wEWHhEernyMkL+LxrK1hYWOCR91O1VGkeN5dXtvfv5ZvqckS/zti0/QAiIyOxY99xlWDI2ZNHD+iC3K451OpEgUEhahsN61bF8vV/Y97SjWkQASIi+i90yFE+4TFiIiJK99o0q4dZEwbGXJeGv9/zAFy5cQf/7D+BVZv+QVBwSMz9OXNkxTcjPkO1iqXUiMG+I2cxZsoCdRKz6fNWYPq8lTGPHdCjDTp/0ggu2Z1gMBhQqXH3mOVJG9Wtil6dmqNEkfzqutejJzh4/BwWr9jCsiIiIjPEpICIiIiISOM4p4CIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0jkkBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBARERERaRyTAiIiIiIijWNSQERERESkcUwKiIiIiIg0ziKtd4CItOfb7dvTehfSjZHWzmm9C+nLtAZpvQfphm7Lk7TeBSLSEI4UEBERERFpHJMCIiIiIiKNY1JARERERKRxTAqIiIiIiDSOSQERERERkcYxKSAiIiIi0jgmBUREREREGsekgIiIiIhI45gUEBERERFpnA45yken9U4QEREREVHa4UgBEREREZHGMSkgIiIiItI4JgVERERERBrHpICIiIiISOOYFBBRhuR1dgvss9im9W6YhcIF8uDYtkXqdxfnrNj4y3fQ0vvh95/GokBet7duY+aEgejRsVkK7B0RUdqzSOsdICKi9OOxzzO06DYSWtK53/i03gUiojTHpICIMrxSxQrimxG9YGOdGWHhERg3bRFOnL2MkV90RlBwKH5cvBZ1a5THsp/GoUazz3DrrhdmjOuPwyfPY92fe5Deery/++l31K9dCc7ZHDF26iIU9HBHk3rVkCWLLYZN+BFHTl5Qj61dtSwG9WqHzJmsEGU0YtKs39QxiSG926NF4/cRGBiM3YdOxWzf3TUHdq6ejaI128c8X5Ga7eAfEKSuX9izHA07DsJ9L281urB+617UqFQKri7ZMXvRGoRHRKBjy4ZwcXbC5NlLsOnvA0gvurRpjIZ1qiCbkwNmLliJ1Zt2qdvlOLoNmoSLVz1RMJ87ZowfgCx2Nrh5+z5sbayx8a99WLP5xWPf88iN1fO/gWvO7Lh64w76jJiKiMjIND4yIqL/juVDRJShWVpYYPH00Zg+byU+aNMf46ctxsJpo1SCcODoOdSsXFo9rlaVsjh57jJqVimjrtesXAYHjp1DehQUHIKmnYdiyLgf8OOkwfB+4otGHQfjux+X4utB3dRj8ri5YEjvDujUbxwadhiEvqOm4edvh8LK0gL1alZA0w9roGH7geq+3K45kr0vEsdmXYajdc8vMW5oD+R0zoZmXYah17Dv8c2Iz5CehIdHoEmnIejUdxwmDu8Fg+HVf4E/TBqM5Ru2o06rvir5qlyueJz7ixf2QNcBE1G7xecqKWvyQbVUPAIiopTDkQIiytAK5HODMdqIfUfOqOvHz16Cz1M/lCicHyfOXkLxwvlVT3rV8iUwYcYv+F+7Jjh47ByCQ0JVKU16tPll7/u5SzdUT/am7fvV9TMXrsEjTy71e53q5eGRO1ec+QHG6Gi45XJGjUqlsWXHAQQGhajbf1+3HZXKFvtP+3L73kM1CrP1n8Mv9u3idTg62Kk6ftMoQ1rbsG2vurxx+z4io4zIkc0JD72fxtxvZ2uN4oU8sHbLi9GhG573ceLMpTjb2L77KEJCw9TvZy5cR173nKl6DEREKYVJARFp0IsTuYdHROLfSzfQ9MPqCA0LU6U1U77ui/erlcPB4+lzlEBI41tERRnjXDdGGWFhMKjfdTpg/7GzaoTgrd5wXvvIyCgY9P/fo54pk2W8fQmP+V2ePzTW9ehoxOxPemCKk2lfDRZv37foeMGJfXwq3onYBhGROWD5EBFlaDdvP4Bep0etl2VBFUoXgXM2J1y4ektdP3DsLIZ93hEHj/2L6OhodXuvzs3TbelQYu09fEaVRhV9L1/MbWVKvBdzzFI+JKMMomOrBq/djowAlC1ZSP3eqG7VmL/JiGTk5NI1T7Rq8r66LisSVSyTvBEUIiJzw5ECIsrQZBJo9yGT1UTjMYO7qd7iXkO/U+VBQuYVfDXwf6pXXew/ehaN61aNmZBrrqQxL6ME33/VF9aZM6m5BJLwyG27D55C2RKF8PeqWa9MNI5v7LRFmDiiF0b0DcE/B07gma8/MrL+X89Uk8z7dGmpYihlUOml/ImIKCXpkKP8GwaOiYiItEMmTpsSxtyuLtiydCoadxwMr8dP0nrXiIhSFEcKiIiIXqpYpqgaORKyOpEsX8uEgIi0gCMFREREREQax4nGREREREQax6SAiIiIiEjjmBQQEREREWkckwIiIiIiIo1jUkBEREREpHFMCoiIiIiINI5JARERERGRxjEpICIiIiLSOCYFRERERETQtv8DRDtDDAmrsssAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot: 1-row heatmap-style scatter\n",
    "# ==================================================================================================\n",
    "colors = [\"skyblue\", \"gold\", \"tomato\"]\n",
    "cmap = ListedColormap(colors)\n",
    "norm = BoundaryNorm([0, 1, 2, 3], cmap.N)\n",
    "stripe_height = 20\n",
    "state_array = np.tile(data[\"encoded_value\"].values, (stripe_height, 1))\n",
    "train_mask = data.index <= pd.to_datetime(end_train)\n",
    "train_indices = np.where(train_mask)[0]\n",
    "test_indices = np.where(~train_mask)[0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 2.5))\n",
    "ax.imshow(\n",
    "    state_array,\n",
    "    aspect=\"auto\",\n",
    "    cmap=cmap,\n",
    "    norm=norm,\n",
    "    extent=[0, len(data) - 1, 0, stripe_height],\n",
    ")\n",
    "\n",
    "ax.hlines(y=30, xmin=train_indices[0], xmax=train_indices[-1], color=\"#30a2da\", linewidth=4)\n",
    "ax.hlines(y=30, xmin=test_indices[0], xmax=test_indices[-1], color=\"#fc4f30\", linewidth=4)\n",
    "ax.set_yticks([])\n",
    "ax.set_frame_on(False)\n",
    "tick_indices = np.linspace(0, len(data) - 1, 10, dtype=int)\n",
    "ax.set_xticks(tick_indices)\n",
    "ax.set_xticklabels([data.index[i].strftime(\"%Y-%m\") for i in tick_indices], rotation=0, ha=\"center\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_title(\"Categorical Daily States Over Time\", pad=8)\n",
    "\n",
    "cbar = fig.colorbar(\n",
    "    plt.cm.ScalarMappable(cmap=cmap, norm=norm),\n",
    "    ax=ax,\n",
    "    orientation=\"horizontal\",\n",
    "    ticks=[0.5, 1.5, 2.5],\n",
    "    pad=0.3,       # space between plot and colorbar\n",
    "    fraction=0.05,  # thickness\n",
    "    aspect=20       # length-to-height ratio\n",
    ")\n",
    "cbar.outline.set_visible(False)\n",
    "cbar.ax.set_xticklabels([\"low\", \"medium\", \"high\"], fontsize=8)\n",
    "cbar.set_label(\"\", labelpad=2, fontsize=9)\n",
    "\n",
    "legend_elements = [\n",
    "    Patch(facecolor=\"#30a2da\", label=\"Train\"),\n",
    "    Patch(facecolor=\"#fc4f30\", label=\"Test\"),\n",
    "]\n",
    "ax.legend(handles=legend_elements, loc=\"upper right\", frameon=False, ncol=2, borderaxespad=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ac667b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot: states timeline\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "color_map = {\"low\": \"skyblue\", \"medium\": \"gold\", \"high\": \"tomato\"}\n",
    "for state, color in color_map.items():\n",
    "    mask = data[\"Demand\"] == state\n",
    "    ax.plot(\n",
    "        data.index[mask], [state] * mask.sum(), '|', color=color, markersize=8, label=state\n",
    "    )\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_title(\"Categorical States Timeline\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9801a209-b601-4d9b-aa3c-a2e03df98c98",
   "metadata": {},
   "source": [
    "## Create and train forecaster\n",
    "\n",
    "When working with categorical time series, a common challenge arises: not only is the target variable categorical, but the lagged values used as predictors are also categorical, as they represent previous observations of the target variable.\n",
    "\n",
    "Most classification algorithms can handle categorical target variables; however, many cannot directly process categorical predictors. In such cases, it is necessary to encode the categorical features into a numerical format prior to training the model.\n",
    "\n",
    "The `features_encoding` parameter in the [`ForecasterRecursiveClassifier`](https://skforecast.org/latest/api/forecasterrecursive#forecasterrecursiveclassifier) class specifies how the categorical features derived from the target series (e.g., lagged values and window features) are encoded before fitting the underlying machine learning model. The parameter accepts the following options:\n",
    "\n",
    "- `auto`: Use a categorical data type if the classifier supports native categorical features (such as LightGBM, CatBoost, XGBoost, or HistGradientBoostingClassifier). Otherwise, fallback to numerical encoding.\n",
    "  \n",
    "- `categorical`: Enforce the use of a categorical data type. This requires a classifier capable of handling native categorical features.\n",
    "  \n",
    "- `ordinal`: Apply ordinal encoding (0, 1, 2, …). In this case, the classifier interprets the encoded values as numeric, implicitly assuming an ordinal relationship among the categories (for example, 'low' < 'medium' < 'high').\n",
    "\n",
    "Proper encoding of categorical features is crucial to ensure that the model can accurately interpret and leverage the information contained in previous observations of the target series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ae82fc",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "The <code>ForecasterRecursiveClassifier</code> class allows the inclusion of window features through the <a href=\"../api/preprocessing.html#skforecast.preprocessing.preprocessing.RollingFeaturesClassification\"><code>RollingFeaturesClassification</code></a> parameter. These features can capture patterns over a specified number of previous time steps, potentially enhancing the model's predictive capabilities.\n",
    "</p>\n",
    "\n",
    "<p>The available statistics are:</p>\n",
    "\n",
    "<ul>\n",
    "    <li><code>proportion</code>: The relative frequency or ratio of each class within the window.</li>\n",
    "    <li><code>mode</code>: The most frequent class (value) observed in the window.</li>\n",
    "    <li><code>entropy</code>: A measure of the diversity or randomness of the classes.</li>\n",
    "    <li><code>n_changes</code>: The number of times the classes changes within the window.</li>\n",
    "    <li><code>n_unique</code>: The count of unique classes present in the window.</li>\n",
    "</ul>\n",
    "\n",
    "To learn more about window features visit the <a href=\"../user_guides/window-features-and-custom-features.html\">Window and custom features</a> user guide.\n",
    "</p>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "349d1b95-5b16-4620-a46e-d595b4187ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </style>\n",
       "    \n",
       "        <div class=\"container-b9c43d7f2a6f4c89bed0f3f7c9cc12b1\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursiveClassifier</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> HistGradientBoostingClassifier</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]</li>\n",
       "                    <li><strong>Window features:</strong> ['roll_proportion_72']</li>\n",
       "                    <li><strong>Window size:</strong> 72</li>\n",
       "                    <li><strong>Series name:</strong> Demand</li>\n",
       "                    <li><strong>Exogenous included:</strong> True</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-27 12:56:13</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-27 12:56:17</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Classification Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Classes:</strong> ['high', 'low', 'medium']</li>\n",
       "                    <li><strong>Class encoding:</strong> {'high': 0, 'low': 1, 'medium': 2}</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Exogenous names:</strong> Temperature, Holiday, Hour_of_day</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('2014-01-01 01:00:00'), Timestamp('2014-10-31 23:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <Hour></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'categorical_features': 'from_dtype', 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst': None, 'l2_regularization': 0.0, 'learning_rate': 0.1, 'loss': 'log_loss', 'max_bins': 255, 'max_depth': None, 'max_features': 1.0, 'max_iter': 100, 'max_leaf_nodes': 31, 'min_samples_leaf': 20, 'monotonic_cst': None, 'n_iter_no_change': 10, 'random_state': 123, 'scoring': 'loss', 'tol': 1e-07, 'validation_fraction': 0.1, 'verbose': 0, 'warm_start': False}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursiveclassifier.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/autoregressive-classification-forecasting.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "============================= \n",
       "ForecasterRecursiveClassifier \n",
       "============================= \n",
       "Estimator: HistGradientBoostingClassifier \n",
       "Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] \n",
       "Window features: ['roll_proportion_72'] \n",
       "Window size: 72 \n",
       "Series name: Demand \n",
       "Classes: ['high', 'low', 'medium'] \n",
       "Number of classes: 3 \n",
       "Exogenous included: True \n",
       "Exogenous names: Temperature, Holiday, Hour_of_day \n",
       "Feature encoding: auto \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Training range: [Timestamp('2014-01-01 01:00:00'), Timestamp('2014-10-31 23:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <Hour> \n",
       "Estimator parameters: \n",
       "    {'categorical_features': 'from_dtype', 'class_weight': None, 'early_stopping':\n",
       "    'auto', 'interaction_cst': None, 'l2_regularization': 0.0, 'learning_rate':\n",
       "    0.1, 'loss': 'log_loss', 'max_bins': 255, 'max_depth': None, 'max_features':\n",
       "    1.0, 'max_iter': 100, 'max_leaf_nodes': 31, 'min_samples_leaf': 20,\n",
       "    'monotonic_cst': None, 'n_iter_no_change': 10, 'random_state': 123,\n",
       "    'scoring': 'loss', 'tol': 1e-07, 'validation_fraction': 0.1, 'verbose': 0,\n",
       "    'warm_start': False} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-27 12:56:13 \n",
       "Last fit date: 2025-11-27 12:56:17 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit forecaster with rolling window features\n",
    "# ==============================================================================\n",
    "window_features = RollingFeaturesClassification(stats=['proportion'], window_sizes=72)\n",
    "forecaster = ForecasterRecursiveClassifier(\n",
    "                 estimator         = HistGradientBoostingClassifier(random_state=123),\n",
    "                 lags              = 24,\n",
    "                 window_features   = window_features,\n",
    "                 features_encoding = 'auto', \n",
    "             )\n",
    "\n",
    "forecaster.fit(\n",
    "    y    = data_train['Demand'], \n",
    "    exog = data_train[['Temperature', 'Holiday', 'Hour_of_day']]\n",
    ")\n",
    "forecaster"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "733cef42-c7bf-42f8-8c3d-8471638599a4",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1bdf4f8-b999-4d97-899c-fbcc2af7066a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-11-01 00:00:00    medium\n",
       "2014-11-01 01:00:00    medium\n",
       "2014-11-01 02:00:00    medium\n",
       "2014-11-01 03:00:00    medium\n",
       "2014-11-01 04:00:00    medium\n",
       "2014-11-01 05:00:00    medium\n",
       "2014-11-01 06:00:00    medium\n",
       "2014-11-01 07:00:00    medium\n",
       "2014-11-01 08:00:00    medium\n",
       "2014-11-01 09:00:00      high\n",
       "2014-11-01 10:00:00      high\n",
       "2014-11-01 11:00:00    medium\n",
       "2014-11-01 12:00:00    medium\n",
       "2014-11-01 13:00:00    medium\n",
       "2014-11-01 14:00:00    medium\n",
       "2014-11-01 15:00:00    medium\n",
       "2014-11-01 16:00:00    medium\n",
       "2014-11-01 17:00:00       low\n",
       "2014-11-01 18:00:00       low\n",
       "2014-11-01 19:00:00       low\n",
       "2014-11-01 20:00:00    medium\n",
       "2014-11-01 21:00:00    medium\n",
       "2014-11-01 22:00:00    medium\n",
       "2014-11-01 23:00:00    medium\n",
       "Freq: h, Name: pred, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(\n",
    "                  steps = 24, \n",
    "                  exog  = data_test[['Temperature', 'Holiday', 'Hour_of_day']]\n",
    "              )\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1b53f4b-be13-4e7b-bfdd-2cf0f3492452",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data_test.loc[predictions.index, 'encoded_value'].plot(ax=ax, label='test')\n",
    "(\n",
    "    predictions\n",
    "    .to_frame()\n",
    "    .assign(encoded_value=predictions.map(encode_mapping))['encoded_value']\n",
    "    .plot(ax=ax, label='predictions')\n",
    ")\n",
    "y_ticks = list(encode_mapping.values())\n",
    "y_labels = list(encode_mapping.keys())\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_title(\"Categorical Time Series (States)\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_ylabel(\"State\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92bce057-008e-462a-9c46-4557013783bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error (accuracy): 0.6666666666666666\n",
      "Classification Report \n",
      "---------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        high       0.00      0.00      0.00         0\n",
      "         low       1.00      0.33      0.50         9\n",
      "      medium       0.68      0.87      0.76        15\n",
      "\n",
      "    accuracy                           0.67        24\n",
      "   macro avg       0.56      0.40      0.42        24\n",
      "weighted avg       0.80      0.67      0.67        24\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "# Prediction metrics\n",
    "# ==============================================================================\n",
    "error_mse = accuracy_score(\n",
    "                y_true = data_test.loc[predictions.index, 'Demand'],\n",
    "                y_pred = predictions\n",
    "            )\n",
    "            \n",
    "print(f\"Test error (accuracy): {error_mse}\")\n",
    "print(\"Classification Report \\n---------------------\")\n",
    "print(\n",
    "    classification_report(\n",
    "        data.loc[predictions.index, 'Demand'],\n",
    "        predictions\n",
    "    )\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "326a2c49",
   "metadata": {},
   "source": [
    "## Predict probabilities\n",
    "\n",
    "`ForecasterRecursiveClassifier` includes the method `predict_proba` which allows to obtain the predicted probabilities for each category at each forecasted time step. Internally, this method calls the `predict_proba` method of the underlying classifier used inside the forecaster.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c36a45",
   "metadata": {},
   "source": [
    "<div role=\"alert\"\n",
    "    style=\"background: rgba(255,145,0,.08); border-left: 6px solid #ff9100;\n",
    "        border-radius: 6px; padding: 10px 12px; margin: 1em 0;\">\n",
    "\n",
    "<p style=\"display:flex; align-items:center; font-size:1rem; color:#ff9100;\n",
    "        margin:0 0 6px 0; font-weight:600;\">\n",
    "<span style=\"margin-right:6px; font-size:18px;\">⚠️</span>\n",
    "<strong style=\"margin-right:6px; font-size:18px;\">Warning</strong>\n",
    "</p>\n",
    "\n",
    "<p style=\"margin:0; color:inherit;\">\n",
    "It is important to note that these probabilities come from the classifier’s <code>predict_proba</code> method. Their calibration and reliability depend on the specific model used. For example, in scikit-learn, <code>predict_proba</code> returns scores that approximate probabilities, but these are not always well-calibrated or truly probabilistic. See section on <a href=\"./py72-classification-forecasting#Probability_calibration\">Probability Calibration</a> for more details.\n",
    "</p>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cab13451",
   "metadata": {},
   "outputs": [
    {
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       "      <th>low_proba</th>\n",
       "      <th>medium_proba</th>\n",
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       "      <td>0.988829</td>\n",
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       "      <th>2014-11-01 12:00:00</th>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.999376</td>\n",
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       "    <tr>\n",
       "      <th>2014-11-01 13:00:00</th>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.999632</td>\n",
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       "    <tr>\n",
       "      <th>2014-11-01 14:00:00</th>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.999792</td>\n",
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       "    <tr>\n",
       "      <th>2014-11-01 15:00:00</th>\n",
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       "      <th>2014-11-01 16:00:00</th>\n",
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       "      <td>0.041339</td>\n",
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       "    <tr>\n",
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       "      <td>0.000035</td>\n",
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       "      <td>0.004677</td>\n",
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       "    <tr>\n",
       "      <th>2014-11-01 19:00:00</th>\n",
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       "      <td>0.023744</td>\n",
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       "    <tr>\n",
       "      <th>2014-11-01 20:00:00</th>\n",
       "      <td>0.000317</td>\n",
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       "      <th>2014-11-01 21:00:00</th>\n",
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      "text/plain": [
       "                     high_proba  low_proba  medium_proba\n",
       "2014-11-01 00:00:00    0.001643   0.001311      0.997046\n",
       "2014-11-01 01:00:00    0.002286   0.004299      0.993415\n",
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       "2014-11-01 03:00:00    0.000601   0.002429      0.996970\n",
       "2014-11-01 04:00:00    0.001399   0.000247      0.998354\n",
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       "2014-11-01 08:00:00    0.212137   0.000529      0.787334\n",
       "2014-11-01 09:00:00    0.559296   0.000397      0.440307\n",
       "2014-11-01 10:00:00    0.869440   0.000120      0.130440\n",
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       "2014-11-01 12:00:00    0.000274   0.000349      0.999376\n",
       "2014-11-01 13:00:00    0.000247   0.000121      0.999632\n",
       "2014-11-01 14:00:00    0.000193   0.000016      0.999792\n",
       "2014-11-01 15:00:00    0.000051   0.000086      0.999863\n",
       "2014-11-01 16:00:00    0.000686   0.007753      0.991561\n",
       "2014-11-01 17:00:00    0.000244   0.958417      0.041339\n",
       "2014-11-01 18:00:00    0.000035   0.995288      0.004677\n",
       "2014-11-01 19:00:00    0.000057   0.976199      0.023744\n",
       "2014-11-01 20:00:00    0.000317   0.474168      0.525515\n",
       "2014-11-01 21:00:00    0.037326   0.001260      0.961414\n",
       "2014-11-01 22:00:00    0.261726   0.001186      0.737088\n",
       "2014-11-01 23:00:00    0.023844   0.000232      0.975924"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict probabilities\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict_proba(\n",
    "                  steps = 24, \n",
    "                  exog  = data_test[['Temperature', 'Holiday', 'Hour_of_day']]\n",
    "              )\n",
    "predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dbe3c4dc-4b71-42f0-a4c9-d6815535272d",
   "metadata": {},
   "source": [
    "## Feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8394cca9-e86d-4309-b10c-ea26485f85e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursive\\_forecaster_recursive_classifier.py:1922: UserWarning: Impossible to access feature importances for estimator of type <class 'sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier'>. This method is only valid when the estimator stores internally the feature importances in the attribute `feature_importances_` or `coef_`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Native feature importance of the estimator used in the forecaster\n",
    "# ==============================================================================\n",
    "forecaster.get_feature_importances()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aa1a1be6",
   "metadata": {},
   "source": [
    "## Backtesting\n",
    "\n",
    "The [backtesting](../user_guides/backtesting.html) process for classification forecasters follows the same principles as that of regression forecasters. The key difference lies in the evaluation metrics used to assess model performance. Instead of using metrics like Mean Squared Error (MSE) or Mean Absolute Error (MAE), classification forecasters utilize metrics such as accuracy, precision, ..., to evaluate how well the model predicts categorical outcomes.\n",
    "\n",
    "\n",
    "The returned predictions will include the most likely category for each forecasted time step in the column `pred` as well as the predicted probabilities for each category in separate columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f15d89f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71bff0ca677144e5a0f9fb94e5911428",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/61 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtesting forecaster\n",
    "# ==============================================================================\n",
    "window_features = RollingFeaturesClassification(stats=['proportion'], window_sizes=72)\n",
    "forecaster = ForecasterRecursiveClassifier(\n",
    "                 estimator         = HistGradientBoostingClassifier(random_state=123),\n",
    "                 lags              = 24,\n",
    "                 window_features   = window_features,\n",
    "                 features_encoding = 'auto',\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(steps = 24, initial_train_size = end_train)\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster    = forecaster,\n",
    "                          y             = data['Demand'],\n",
    "                          exog          = data[['Temperature', 'Holiday', 'Hour_of_day']],\n",
    "                          cv            = cv,\n",
    "                          metric        = 'accuracy_score',\n",
    "                          n_jobs        = 'auto',\n",
    "                          verbose       = False,\n",
    "                          show_progress = True\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "adc4c70f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>59</td>\n",
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       "      <td>0.000015</td>\n",
       "      <td>0.990844</td>\n",
       "      <td>0.009141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 22:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.460920</td>\n",
       "      <td>0.538982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 23:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.990127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31 00:00:00</th>\n",
       "      <td>60</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.001948</td>\n",
       "      <td>0.002009</td>\n",
       "      <td>0.996042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1441 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fold    pred  high_proba  low_proba  medium_proba\n",
       "2014-11-01 00:00:00     0  medium    0.001643   0.001311      0.997046\n",
       "2014-11-01 01:00:00     0  medium    0.002286   0.004299      0.993415\n",
       "2014-11-01 02:00:00     0  medium    0.000829   0.001231      0.997940\n",
       "2014-11-01 03:00:00     0  medium    0.000601   0.002429      0.996970\n",
       "2014-11-01 04:00:00     0  medium    0.001399   0.000247      0.998354\n",
       "...                   ...     ...         ...        ...           ...\n",
       "2014-12-30 20:00:00    59     low    0.000042   0.746859      0.253099\n",
       "2014-12-30 21:00:00    59     low    0.000015   0.990844      0.009141\n",
       "2014-12-30 22:00:00    59  medium    0.000097   0.460920      0.538982\n",
       "2014-12-30 23:00:00    59  medium    0.000414   0.009459      0.990127\n",
       "2014-12-31 00:00:00    60  medium    0.001948   0.002009      0.996042\n",
       "\n",
       "[1441 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(metric)\n",
    "display(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6ed6bbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report \n",
      "---------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        high       0.50      0.36      0.42       107\n",
      "         low       0.84      0.83      0.84       364\n",
      "      medium       0.87      0.90      0.89       970\n",
      "\n",
      "    accuracy                           0.84      1441\n",
      "   macro avg       0.74      0.70      0.71      1441\n",
      "weighted avg       0.84      0.84      0.84      1441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Summary of classification metrics on test set\n",
    "# ==============================================================================\n",
    "print(\"Classification Report \\n---------------------\")\n",
    "print(\n",
    "    classification_report(\n",
    "        data.loc[predictions.index, 'Demand'],\n",
    "        predictions['pred']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "207b007d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data.loc[predictions.index, 'encoded_value'].plot(ax=ax, label='test')\n",
    "predictions.assign(encoded_value=predictions['pred'].map(encode_mapping))['encoded_value'].plot(ax=ax, label='predictions')\n",
    "y_ticks = list(encode_mapping.values())\n",
    "y_labels = list(encode_mapping.keys())\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_title(\"Categorical Time Series (States)\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_ylabel(\"State\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9877c816",
   "metadata": {},
   "source": [
    "### Custom metrics during backtesting\n",
    "\n",
    "The `backtesting_forecaster` allows the use of [custom metrics](../user_guides/backtesting.html#backtesting-with-custom-metric) to evaluate forecaster performance.\n",
    "\n",
    "This is particularly useful for classification tasks, as it enables metrics to be tailored to the specific problem type (e.g., binary, multi-class, or multi-label). Common examples include [`f1_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), [`roc_auc_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html), and [`precision_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79ede94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom f1-score for multi-class classification\n",
    "# ==============================================================================\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "def custom_f1_score(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Custom f1-score metric, it must have `y_true` and `y_pred` as parameters.\n",
    "    \"\"\"\n",
    "    return f1_score(y_true, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228642af",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning\n",
    "\n",
    "[Hyperparameter tuning](../user_guides/hyperparameter-tuning-and-lags-selection.html) for `ForecasterRecursiveClassifier` can be performed using the functions available in the `skforecast.model_selection` module, such as `grid_search_forecaster`, `random_search_forecaster` and `bayesian_search_forecaster`. These functions allow for systematic exploration of hyperparameter combinations to identify the best configuration for the forecaster based on specified evaluation metrics suitable for classification tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69bb1667",
   "metadata": {},
   "source": [
    "When performing hyperparameter tuning, it is important to use a train-validation-test split approach. This involves dividing the dataset into three distinct subsets:\n",
    "\n",
    "- Training set: Used to fit the model.\n",
    "- Validation set: Used to tune the hyperparameters.\n",
    "- Test set: Used to evaluate the model's performance.\n",
    "\n",
    "This approach helps to prevent overfitting and ensures that the model generalizes well to unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "62034714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates      : 2014-01-01 01:00:00 --- 2014-08-31 23:00:00  (n=5831)\n",
      "Validation dates : 2014-09-01 00:00:00 --- 2014-10-31 23:00:00  (n=1464)\n",
      "Test dates       : 2014-11-01 00:00:00 --- 2014-12-31 00:00:00  (n=1441)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train-validation-test split\n",
    "# ==============================================================================\n",
    "end_train = '2014-08-31 23:59:00'\n",
    "end_val = '2014-10-31 23:59:00'\n",
    "print(\n",
    "    f\"Train dates      : {data.index.min()} --- {data.loc[:end_train].index.max()}\"\n",
    "    f\"  (n={len(data.loc[:end_train])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Validation dates : {data.loc[end_train:].index.min()} --- {data.loc[:end_val].index.max()}\"\n",
    "    f\"  (n={len(data.loc[end_train:end_val])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Test dates       : {data.loc[end_val:].index.min()} --- {data.index.max()}\"\n",
    "    f\"  (n={len(data.loc[end_val:])})\"\n",
    ")\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2280ed44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╭─────────────────────────── OneStepAheadValidationWarning ────────────────────────────╮</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> One-step-ahead predictions are used for faster model comparison, but they may not    <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> fully represent multi-step prediction performance. It is recommended to backtest the <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> final model for a more accurate multi-step performance estimate.                     <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>                                                                                      <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Category : skforecast.exceptions.OneStepAheadValidationWarning                       <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Location :                                                                           <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\model_s <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> election\\_utils.py:607                                                               <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Suppress : warnings.simplefilter('ignore', category=OneStepAheadValidationWarning)   <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╰──────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;214m╭─\u001b[0m\u001b[38;5;214m──────────────────────────\u001b[0m\u001b[38;5;214m OneStepAheadValidationWarning \u001b[0m\u001b[38;5;214m───────────────────────────\u001b[0m\u001b[38;5;214m─╮\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m One-step-ahead predictions are used for faster model comparison, but they may not    \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m fully represent multi-step prediction performance. It is recommended to backtest the \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m final model for a more accurate multi-step performance estimate.                     \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m                                                                                      \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Category : skforecast.exceptions.OneStepAheadValidationWarning                       \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Location :                                                                           \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\model_s \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m election\\_utils.py:607                                                               \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Suppress : warnings.simplefilter('ignore', category=OneStepAheadValidationWarning)   \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m╰──────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
      ]
     },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
      "  Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] \n",
      "  Parameters: {'max_iter': 200, 'max_depth': 8, 'learning_rate': 0.06995250096123734, 'l2_regularization': 0.3790013234407905}\n",
      "  One-step-ahead metric: 0.9050546448087432\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lags</th>\n",
       "      <th>params</th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>max_iter</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>learning_rate</th>\n",
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       "      <th>0</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_iter': 200, 'max_depth': 8, 'learning_ra...</td>\n",
       "      <td>0.905055</td>\n",
       "      <td>200.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.069953</td>\n",
       "      <td>0.379001</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_iter': 200, 'max_depth': 8, 'learning_ra...</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.099421</td>\n",
       "      <td>0.175452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_iter': 400, 'max_depth': 7, 'learning_ra...</td>\n",
       "      <td>0.902322</td>\n",
       "      <td>400.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.059226</td>\n",
       "      <td>0.412988</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_iter': 200, 'max_depth': 9, 'learning_ra...</td>\n",
       "      <td>0.901639</td>\n",
       "      <td>200.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.096356</td>\n",
       "      <td>0.215461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                lags  \\\n",
       "0  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "1  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "2  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "3  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "\n",
       "                                              params  accuracy_score  \\\n",
       "0  {'max_iter': 200, 'max_depth': 8, 'learning_ra...        0.905055   \n",
       "1  {'max_iter': 200, 'max_depth': 8, 'learning_ra...        0.903689   \n",
       "2  {'max_iter': 400, 'max_depth': 7, 'learning_ra...        0.902322   \n",
       "3  {'max_iter': 200, 'max_depth': 9, 'learning_ra...        0.901639   \n",
       "\n",
       "   max_iter  max_depth  learning_rate  l2_regularization  \n",
       "0     200.0        8.0       0.069953           0.379001  \n",
       "1     200.0        8.0       0.099421           0.175452  \n",
       "2     400.0        7.0       0.059226           0.412988  \n",
       "3     200.0        9.0       0.096356           0.215461  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bayesian search hyperparameters and lags with Optuna\n",
    "# ==============================================================================\n",
    "estimator = HistGradientBoostingClassifier(\n",
    "                early_stopping= True,\n",
    "                random_state=123,\n",
    "                verbose=0\n",
    "            )\n",
    "forecaster = ForecasterRecursiveClassifier(\n",
    "                 estimator         = estimator,\n",
    "                 lags              = 14,  # Placeholder, the value will be overwritten\n",
    "                 window_features   = RollingFeaturesClassification(stats=['proportion'], window_sizes=[72]),\n",
    "                 features_encoding = 'auto',\n",
    "             )\n",
    "\n",
    "# Lags used as predictors\n",
    "lags_grid = [24, (1, 2, 3, 23, 24, 25, 47, 48, 49)]\n",
    "\n",
    "# Search space\n",
    "def search_space(trial):\n",
    "    search_space  = {\n",
    "        'lags': trial.suggest_categorical('lags', lags_grid),\n",
    "        'max_iter': trial.suggest_int('max_iter', 100, 500, step=100),\n",
    "        'max_depth': trial.suggest_int('max_depth', 3, 10),\n",
    "        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.5),\n",
    "        'l2_regularization': trial.suggest_float('l2_regularization', 0.0, 1.0)\n",
    "    } \n",
    "    return search_space\n",
    "\n",
    "# Folds\n",
    "cv_search = OneStepAheadFold(initial_train_size = end_train)\n",
    "\n",
    "results, best_trial = bayesian_search_forecaster(\n",
    "                          forecaster          = forecaster,\n",
    "                          y                   = data.loc[:end_val, 'Demand'],\n",
    "                          exog                = data.loc[:end_val, ['Temperature', 'Holiday', 'Hour_of_day']],\n",
    "                          search_space        = search_space,\n",
    "                          cv                  = cv_search,\n",
    "                          metric              = 'accuracy_score',\n",
    "                          kwargs_create_study = {'direction': 'maximize'},\n",
    "                          n_trials            = 20,\n",
    "                          random_state        = 123,\n",
    "                          return_best         = True,\n",
    "                          n_jobs              = 'auto',\n",
    "                          verbose             = False,\n",
    "                          show_progress       = True,\n",
    "                      )\n",
    "results.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ade16350",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Best model\n",
    "# ==============================================================================\n",
    "best_params = results.loc[0, 'params']\n",
    "best_lags   = results.loc[0, 'lags']\n",
    "\n",
    "forecaster = ForecasterRecursiveClassifier(\n",
    "    estimator       = HistGradientBoostingClassifier(random_state=123, **best_params),\n",
    "    lags            = best_lags,\n",
    "    window_features = RollingFeaturesClassification(stats=['proportion'], window_sizes=[72]),\n",
    "    features_encoding = 'auto',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bfba2d79",
   "metadata": {},
   "outputs": [
    {
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    },
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       "      <th></th>\n",
       "      <th>accuracy_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "   accuracy_score\n",
       "0        0.840389"
      ]
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     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "      <th>high_proba</th>\n",
       "      <th>low_proba</th>\n",
       "      <th>medium_proba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.001048</td>\n",
       "      <td>0.001395</td>\n",
       "      <td>0.997558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.002579</td>\n",
       "      <td>0.996947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>0.998666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.996516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>0.999497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 20:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>low</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.697114</td>\n",
       "      <td>0.302691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 21:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>low</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.994279</td>\n",
       "      <td>0.005701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 22:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.375040</td>\n",
       "      <td>0.624822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 23:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.994000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31 00:00:00</th>\n",
       "      <td>60</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>0.996721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1441 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fold    pred  high_proba  low_proba  medium_proba\n",
       "2014-11-01 00:00:00     0  medium    0.001048   0.001395      0.997558\n",
       "2014-11-01 01:00:00     0  medium    0.000473   0.002579      0.996947\n",
       "2014-11-01 02:00:00     0  medium    0.000147   0.001187      0.998666\n",
       "2014-11-01 03:00:00     0  medium    0.000296   0.003188      0.996516\n",
       "2014-11-01 04:00:00     0  medium    0.000237   0.000265      0.999497\n",
       "...                   ...     ...         ...        ...           ...\n",
       "2014-12-30 20:00:00    59     low    0.000195   0.697114      0.302691\n",
       "2014-12-30 21:00:00    59     low    0.000020   0.994279      0.005701\n",
       "2014-12-30 22:00:00    59  medium    0.000138   0.375040      0.624822\n",
       "2014-12-30 23:00:00    59  medium    0.000092   0.005908      0.994000\n",
       "2014-12-31 00:00:00    60  medium    0.000738   0.002541      0.996721\n",
       "\n",
       "[1441 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest final model on test data\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(steps = 24, initial_train_size = end_val)\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster = forecaster,\n",
    "                          y          = data['Demand'],\n",
    "                          exog       = data[['Temperature', 'Holiday', 'Hour_of_day']],\n",
    "                          cv         = cv,\n",
    "                          metric     = 'accuracy_score'\n",
    "                      )\n",
    "display(metric)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63e0422a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report \n",
      "---------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        high       0.46      0.44      0.45       107\n",
      "         low       0.86      0.81      0.84       364\n",
      "      medium       0.87      0.89      0.88       970\n",
      "\n",
      "    accuracy                           0.84      1441\n",
      "   macro avg       0.73      0.72      0.72      1441\n",
      "weighted avg       0.84      0.84      0.84      1441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Summary of classification metrics on test set\n",
    "# ==============================================================================\n",
    "print(\"Classification Report \\n---------------------\")\n",
    "print(\n",
    "    classification_report(\n",
    "        data.loc[predictions.index, 'Demand'],\n",
    "        predictions['pred']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80297ef5",
   "metadata": {},
   "source": [
    "## Probability calibration\n",
    "\n",
    "Well calibrated classifiers are probabilistic classifiers for which the output of the `predict_proba` method can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that among the samples to which it gave a `predict_proba` value close to, say, 0.8, approximately 80% actually belong to the positive class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea91331",
   "metadata": {},
   "source": [
    "Before we show how to re-calibrate a classifier, we first need a way to detect how good a classifier is calibrated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7dcb46b",
   "metadata": {},
   "source": [
    "### Calibration curves\n",
    "\n",
    "The following definition of calibration curves is taken from [scikit-learn documentation](https://scikit-learn.org/stable/modules/calibration.html#calibration-curves):\n",
    "\n",
    "\"Calibration curves, also referred to as reliability diagrams, compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the frequency of the positive label (to be more precise, an estimation of the conditional event probability ) on the y-axis against the predicted probability predict_proba of a model on the x-axis. The tricky part is to get values for the y-axis. In scikit-learn, this is accomplished by binning the predictions such that the x-axis represents the average predicted probability in each bin. The y-axis is then the fraction of positives given the predictions of that bin, i.e. the proportion of samples whose class is the positive class (in each bin).\"\n",
    "\n",
    "Since calibration curves are defined for binary classifiers, in the case of multi-class classification problems, one-vs-rest calibration curves are used. This means that for each class, the samples belonging to that class are considered as positive samples, while all other samples are treated as negative samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f8d8348a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calibration plots: one-vs-rest\n",
    "# ==============================================================================\n",
    "for level, code in {'low': 0, 'medium': 1, 'high': 2}.items():\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(6, 2.5))\n",
    "\n",
    "    y_prob = predictions.loc[:, f'{level}_proba'].rename(code)\n",
    "    y_true = (data.loc[predictions.index, 'encoded_value'] == code).astype(int)\n",
    "    curve_prob_true, curve_prob_pred = calibration_curve(\n",
    "        y_true=y_true,\n",
    "        y_prob=y_prob,\n",
    "        n_bins=10\n",
    "    )\n",
    "    \n",
    "    disp = CalibrationDisplay(\n",
    "        prob_true=curve_prob_true, prob_pred=curve_prob_pred, y_prob=y_prob\n",
    "    )\n",
    "    disp.plot(name=level, ax=ax, color='C' + str(code))\n",
    "\n",
    "    ax.set_title(f\"{level}\")\n",
    "    ax.set_xlabel(\"Mean predicted probability\")\n",
    "    ax.set_ylabel(\"Fraction of positives\")\n",
    "    ax.get_lines()[0].set_color('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78137a41",
   "metadata": {},
   "source": [
    "The calibrations curves show that the classifier is not well calibrated, as the curves deviate significantly from the diagonal line representing perfect calibration. For example, for the class 'low', when the classifier predicts a probability of 0.8, the actual frequency of the positive class is only around 0.5, indicating overconfidence in its predictions for that class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fc9c81",
   "metadata": {},
   "source": [
    "### Calibration\n",
    "\n",
    "The calibration of a classification model refers to the process of adjusting the predicted probabilities so that they align with the actual observed outcomes. In other words, calibration corrects the model when it tends to systematically overestimate or underestimate probabilities.\n",
    "\n",
    "A model is said to be perfectly calibrated if, for any predicted probability $p \\in [0, 1]$, the proportion of correctly classified observations among those predicted with probability $p$ is exactly $p$. Formally:\n",
    "\n",
    "$$\n",
    "P(\\hat{Y} = Y \\mid \\hat{P} = p) = p, \\quad \\forall p \\in [0, 1]\n",
    "$$\n",
    "\n",
    "where:  \n",
    "- $\\hat{Y}$ is the predicted class,  \n",
    "- $Y$ is the true class, and  \n",
    "- $\\hat{P}$ is the predicted probability of the positive (or target) class.\n",
    "\n",
    "For example, if we look at all cases where the model predicts a probability of $\\hat{P} = 0.8$, we would expect that about 80% of those cases truly belong to the positive class.  \n",
    "\n",
    "When a model is perfectly calibrated, plotting the observed frequencies against the predicted probabilities produces a **diagonal line** (the identity function) across the range $[0, 1]$:\n",
    "\n",
    "$$\n",
    "\\text{Observed frequency} = \\text{Predicted probability}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7b9204",
   "metadata": {},
   "source": [
    "The [`CalibratedClassifierCV`](https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html) class in sklearn is used to calibrate a classifier. Since skforecast is compatible with scikit-learn, `CalibratedClassifierCV` can be used directly into [`ForecasterRecursiveClassifier`](https://skforecast.org/0.18.0/api/forecasterrecursive#forecasterrecursiveclassifier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4b0a602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ef3b13e728740f2acb833e38c343bce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/61 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>accuracy_score</th>\n",
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       "      <th>0</th>\n",
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      "text/plain": [
       "   accuracy_score\n",
       "0        0.843164"
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     "metadata": {},
     "output_type": "display_data"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
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       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0</td>\n",
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       "      <th>2014-11-01 02:00:00</th>\n",
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       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.018259</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.014561</td>\n",
       "      <td>0.012693</td>\n",
       "      <td>0.972746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 20:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>low</td>\n",
       "      <td>0.001329</td>\n",
       "      <td>0.627120</td>\n",
       "      <td>0.371551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 21:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>low</td>\n",
       "      <td>0.001431</td>\n",
       "      <td>0.913995</td>\n",
       "      <td>0.084574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 22:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.000931</td>\n",
       "      <td>0.426978</td>\n",
       "      <td>0.572091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 23:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.003846</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.897678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31 00:00:00</th>\n",
       "      <td>60</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.027835</td>\n",
       "      <td>0.060985</td>\n",
       "      <td>0.911179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1441 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fold    pred  high_proba  low_proba  medium_proba\n",
       "2014-11-01 00:00:00     0  medium    0.022222   0.022406      0.955372\n",
       "2014-11-01 01:00:00     0  medium    0.020423   0.065693      0.913884\n",
       "2014-11-01 02:00:00     0  medium    0.009767   0.044904      0.945330\n",
       "2014-11-01 03:00:00     0  medium    0.018259   0.100651      0.881091\n",
       "2014-11-01 04:00:00     0  medium    0.014561   0.012693      0.972746\n",
       "...                   ...     ...         ...        ...           ...\n",
       "2014-12-30 20:00:00    59     low    0.001329   0.627120      0.371551\n",
       "2014-12-30 21:00:00    59     low    0.001431   0.913995      0.084574\n",
       "2014-12-30 22:00:00    59  medium    0.000931   0.426978      0.572091\n",
       "2014-12-30 23:00:00    59  medium    0.003846   0.098475      0.897678\n",
       "2014-12-31 00:00:00    60  medium    0.027835   0.060985      0.911179\n",
       "\n",
       "[1441 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Forecaster with calibrated classifier\n",
    "# ==============================================================================\n",
    "estimator = CalibratedClassifierCV(\n",
    "                estimator = HistGradientBoostingClassifier(\n",
    "                                **best_params,\n",
    "                                random_state=123\n",
    "                            ),\n",
    "                method   = 'sigmoid',\n",
    "                ensemble = False,  # False to speed up prediction\n",
    "                cv       = 3,\n",
    "            )\n",
    "\n",
    "forecaster = ForecasterRecursiveClassifier(\n",
    "    estimator       = estimator,\n",
    "    lags            = best_lags,\n",
    "    window_features = RollingFeaturesClassification(stats=['proportion'], window_sizes=[72]),\n",
    "    features_encoding= 'auto',\n",
    ")\n",
    "\n",
    "\n",
    "cv = TimeSeriesFold(steps = 24, initial_train_size = end_val)\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster = forecaster,\n",
    "                          y          = data['Demand'],\n",
    "                          exog       = data[['Temperature', 'Holiday', 'Hour_of_day']],\n",
    "                          cv         = cv,\n",
    "                          metric     = 'accuracy_score'\n",
    "                      )\n",
    "display(metric)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3990f988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/l+/APyBQu7UkIiJSMTIG8hQD8pcC8pcGcipHQhLMOCNgkUN5iY+YtRc9oFJd9/VCRIAPwgzCEfnZB0DSP8RFj5LogBAXIYe1JUoXL4SeHZvj733/yZwt8ehL9l8CFFmAHFGzDLNlB+zbATnyKm/fOg2c3RZv0EcpaCVykUh+/NyXTYSJiIi0TqEAcuRXBkwFSgG5i8RMGPf4CFjZfvuxNswAAnyALBaAmQWQxTzq51e3RZAlEs7FxSZPjIcRy0++F1dEUrf/56gAK1qg9XXgFeQf4zHGDu2BUYO6of+YmThz4bos27z7KIKDQ7Dl1PVvzyoUxOMe/gd4cfvbr530H0DdPrkeh09fwqGTF3Hj7hPt1oiIiMjc+ksPU/6SMdd3EjPpHB8pk6TFRQQ9/Wd9+3HDwwBvN+UlPhlNvwRT6gDLPFqgJS7ZlMNoqnPii99CQ2L0ZDnmLItLfplQuWVbnHnwRgZgIiFcDt3lyPft4EnYvwJwfPDt8yhlBFBXbz1E1zYN0a9LC3xy9ZSB1MFTDrj78IX2a0hERGmfSWYgX0llD5MInL4eXgsOBN4+Ad48BBwfAp6fYg7riQTqb60DJYblEiI4QHnxcI4zDyeXhQU+hIQj1DRb7D1ZqutihpxxBsDCRnmJsi8Y2Hf8A4DcwNCFysJAf2WQJVYNTwjRm0apJ4AaNmkhTDJmQMMfqqB149ro3bkZBvVsjffOrjh40kEGVI+eOWq/tkRElDYYGgO5C0cNy4k8pvyAwkCzp8j5lTJYEj1Mzq+BSOUaSLESPVJikcxkXIlcISZP+fsAPl4xjonE73BVgrOhMTasX4pCpUtj4abD2O9wL1qPlgi8LJW3M4hhw8zKC6XdHKig4BAcPnVJXsQUyyZ1q8ntWwb1aIPhfTvA8d1H/NDux0Q/bt8uzfFjn/awtrLA4+eO+GX+qnh7tgb2aI0+nZrJPYC8vH1w+PRlzP1zAxfyJCJKURTKXCJVwJSnqDLPKDoxe0w1JPfuacJ7YVREcKTnRTLNsmTG0tnj0LhudRSp0Q6BgcFAeChWL1slj53876oybyo2GTN96bWyKwz80CG5q0/JlUQePZlcLFtw8vx1dGndABNG9JIz8xKrdWN7TB83EBPnLMftB88xqEdrbF0xE7XbDIWHl3KV1ejaNauDyT/1wbhf/8SNe09QKJ8dFs8YJWczzFi0VhsvjYiIvmd7FVXid75SQOasmsdFTpCqh0lcxO1UKHpvk9hOpU7NirCztUHzBvbYc/iMLL90/d63H0gMU4qLGDYUvWUMoNJ2ACV6nxrXqYpWje1Rr2YlZMhghDdOn7B2m0OiH2twr7bYuvcEdhxQvuEmzF6BBrWroFvbRvjr35gLc1YuV1wmse87dl7ednJ2lYFcxTLFvvdlERGlT9+zIa/oQclbQtnDJAKnr2fFiR4l0bMkgiUROLmL/J/Uyy5XDixeNAl5c9uiYsMeskx8gR/1yyKZDH7l5n19V5FSWgCVMYOxDGzEkF0D+0oyiBL5T2u3HcLBEw54+Ox1oh/T2MgIZUsU1ljBXLwRHa7dRaWysQdEN+89RfsWdVG+dBE5zJfXLgca2FfG7iPn4nweQ4UChtFWctUmY0NDjZ+UfNj2+sO2TzttH2lmidBBc7+ZiG38zyQofD0RaWCIyFyFEJG/JCLzlUKkbUGxsNGXcyPCofjoCMXbRzB48wgK59dQRN/QNhW+Z4yMDBEWFi7b3MfXD43qVodpJhNULF0MD5+8lOecPHNZnWieVBGGhnKphG/Wx9AQBqmwHVPy/5yELs6ZpADqwbnNMmhycfPE5j0nZNB05+FzfA9Li6zyjenmoZmM5+7hjcL5o3aU/oroebI0z4r9/86HAgoYGxthw86jWLZ2V5zPY2ZiAgtTU+iSjZmZTh+f4sa21x+2fepv+2CrXHBOwIa8mWq2QKhFTgTZFkLkV3lMxt6uMPnwDJmcniOT80sYhEbLY8qaet8jpUsWxsSfB8LX1x8/jlIulyCuT562BE+evYbXR3e5Ora2hBkp4BQWikgxuzAOirBQ2BopYKTF501NbHT0P8fRw0N3AdTOg2dw8MRFXL/7GPpUo3JpjBzQCZN/+1tuXpw/jy1m/W8wXAZ5Ysk/O2K9j29QEAJCQnRSHxENi1+oq68vQtPztgp6wLbXH7Z92mn7iIzZEnSeT7n6X274+8Dg7WMo3j5W9jL5eiJYBGMAUmdGU8zeJiGruwdqVq8gF7n0iwhDgH+gbPs12w/q5n3v7Q2jfyYiUiyBEAdFgC9cfD2R3hinkP85SQqgfpm/WusV8fTykW9UMfsuuuxW5nBzjzlFVPjfsJ5yS5mt+07K209fvpVdqQumjsDSNTvVS+NHFx4Z+WVqqY6IX2i63pdKj9j2+sO2TwNtn9DHcHoBPLupXJPJ1QkRUP6vTSu//ab1a2L2xB9x+sJ1TJy9TJbde/ISQ37+DcfOXoKb12f18JxO3/cJWfAzHQvV8/+cBAVQ1SqWkj+v3X6kcftbVOcnRGhYGO4/eQn7qmXV28OIzYntq5bD+u1HYr2PGEaM+Go6qOq2aoNjIiLSspMbAZe3SEuiz6TLmNEYlcqVQHZLc0ya85f6s2T1pr16riWlJAkKoPas+U2+gQpW6ygDHdXtuKiClzyV2iaqMqs37ceSWWNw7/FLmVMl1pQSPUrbD5yWx5fOGiN3op67bKO8ferCdQzu2RYPn76Wyx6IpRPGD+shy78OrIiIiL7Wr1trTB7VD/OWrcfaLQdk2ZFTFzF84nxs33+SX8Tp+wKojoOmyJ8ieIp+W9sOnrwIK4tsGP9jD1hnt8CjZ6/RY9h0uHsqR9LtbK0REe3NLPKcxJv7f8N7IqeNlRwGFMHTvL826aR+RERpmliGII2LntckiM+cwgXyoGvbxuoAShxf8W/ck5GIBAVsKjG81hIxJi5mYXzw9mYuSDJj2+sP2z6NtL1Y+6nnVCB7AhZBXjc1VQ7hTRkzACP6d0KXwZNx4cptWWZjbYmm9Wpg9+EzCAhI+MrnfN/rT0pp+yQtiLRz9WyZqxSXmpXLyHOIiCgVEDO9uk9UBk/fGrJKzIa8eiaWtokuX+6cyGmTXfY2qbi6eWLjziOJCp6IkjwLTwRI2/adivN4dstsqFGpNFuYiCily5wN6DYBsM6t3ErlwEogOCDZNuTVBQMDAyyfNwFd2jRC2Xrd4OTsIsuXrN6GI6cvyhwnIr1t5RJfYl3+vLngFxCY1IcmIqLkkMVc2fNklQvw8QS2zQM8PyE1Er1NoaHKPF0xiah44fywMM+Kjq0aYMmqrbL88bPX8kKUrAFUp1b15UVl1KAu6N7+SzeoSjazLChRJD/OXryplQoSEZEOmFkqgyfLnMBnd2DrPMDbFamNCJJWzJ+I2tXKo2DVNggJCZXlv8xbIYOq85eVuU5EegugxJpLYraCShbTTDGWChC9UgGBwdi0+xj+WLVduzUlIiLtbRjcfRJgYaNcqHHrXGUQlQp7mz77+KFWlbKws7VBwx+q4ujpS7L80vV7eq4lpXUJDqA27jomL8LVI2sw7ffVOHn+ui7rRkRE2mZuDXSbqPzp5aLseUrhOU0qYrmBP+f8LHesqNKktywTX+SH/m8uPrq4486DZ/quIqUjScqBqt5ioPZrQkREumWRQzlsJ3qgRK7TlrmAX+xbZaUUGTIYq4flPL19UN++CjJmzICihfLh+SvlUgqqXieiFBdA2eW0lj8/fHLTuP0tqvOJiEjPRK6TGLYzswDcnZXDdv6fkVLVqFwWi2aMxrsPn9B18GRZ5un1GX1++hU37z7GqzdO+q4ipXMJCqCuHV2jsZWL6va3JHYrFyIi0gExy070PIlZd67vgW3zgQAfpOTepoDAIBlElS1ZBJlNM8E/amb3jv3KzeOJUkUANfbXP2XApNrKRXWbiIhSOLG+k8h5ypwVcHmnDJ4CfZGStG1WF3MmDcPeo+cwdd5KWXbv0XMMGDNLrtmkCp6IUl0AtfPgmXhvExFRCmSTV7lIplhp/NMbYPvvKWIVcbHQpYGBQr0nndifrmSxgjA0NFQHUMK6rcq96YhSoiRt5RIXYyMjudwBERHpWc78ymE7ETw5v1IukpkCgqdh/Trhzc2D6N6+qbrs4IkLGDh2lnpmHVGaDaDaNKmNX3/WnIk3dkhXvLiyE08dtmPtH5NhmslEW3UkIqLEyFVQ2fMkNgh2eqHseQqKZ3sWHRIz5qIzz2aGPHY50bl1I3WZyHtau+UAfP389VBDomQMoIb0bgfTTF96miqXK46xQ7rh/OU7+GfLAdStWRGjBnZOYpWIiCjJ7AoDXScAJpmB98+AHQuAYP3kEP02ZTic7x1D9cplNIblOg+aiPb9x+ulTkR6DaDEjtZPnr9R327XrA5c3b3Qf+wczF6yHht2HEXzhjW1VkkiIkqA3EWBLuOBjJmAt4+BHQuBkCC99TbZ2mSHpUU2jd6mT64e2HXwtHq2HVG6CqAyZjBGcLQ3/w81KuDcpVsID1du7fL89TvkssmuvVoSEVH88pb4Ejw5PgJ2/gGEBifLU4sk8A3LZsD10UnktLFSly9cuRlNu47Ez78uSZZ6EKX4AOrdBxfUrlZOXi9bsjAK5LHFuWgbNma3Mod/IKedEhElh4h8JYHOY4EMGYHX94HdfwBhIcnW2yRm0xUukBtZzbKgTdO66vJHT1/hxLkrMfZNJUq3W7ls3n0cM/83CEUK5oFtjuz46OKB0xduqI9XKV8Sz16902Y9iYgoFgG5iyOscX/AyBh4eRfYuwwI193wmHV2C6xeOEX+ny9QpbV6U98Js5YpF1q+9VBnz02U6gOoddsPIygkBA3sK+PBk1dY/u8eBAUrv+2YZ80CGytzbNp9XNt1JSKiaCIKlYNLkwGAoRHw/BawfzkQrgxotMnEJCOCgpTDgZ5ePqhaoRRy5bRG7eoVcNZB+eX54rW7Wn9eopRMAZtKXFJcSzIYGsLO3BwfvL0REq5cII6SB9tef9j2elK0EtB2uAyeFM9uIHL/CiBCu+1fomgBrJg3ESYmGVCjeT91ebMGtfDmvTOePHdEesX3vf6klLZPUg9UdGIYL7etjbzu9NEVL16/10a9iIgoLsWqAG1+lMFT5le3EXJoFUK1FDxF721y9/RGrarl5Krh+fLY4u37j7L82JlLWnkuotQsyQFUk7rVMH3cAOTJpQyeoieYz1i0FifPX9dG/YiIKLoS1YDWQwEDQxg8ugLry7vhrIXgqW6tSlg8cywePn2FXsOnyTI3dy/0GPYLrtx8ACdnFy1UniidB1D17Svhn4WTZI/TvGWb8MJR2etUpEAe9OjQBGsWTUbvn2biv2gz84iI6DuVqgm0HCw2kwPuX4DhifVQZMuWpIdSKBRyJp2qt8nPPxDlSxdDvty2sjw4Kq9VrNlERFpaxmD0oK548uINGnQaieXr98jeJnER10XZ05dv5MrkRESkJWVqA62igqe7/wFH1kIRmbQU1q7tmuDltX343/Ave8/dvPsYvUdMR6FqbdXBExFpOYAqWTQ/dh46g8Coby7RibIdB8/Ic4iISAvK1wVaDgIUBsDtM8CxfwEkPHgyNjaSi12qKBRAwXy50b5FPY3zNu06Ai9vH61WnSitSlIAJZYssMhmFudxcUy1rAEREX2Hig2AZv2V12+cAE5sSFTw9POwXnC6exQdWzVUl+09ck7mOdVo8WVmHRElQwB16cZ9DOjeCpXKFotxrELpoujfrRUuXruXlIdG3y7Nce3oGry+tgeHNy1E+dJF4j0/q1lm/DZpKO6c2gDH63vhcOBvmaNFRJTqVW4MNOmjvH7tKHB6yzfvYmpqonE7s2km2GS3RIcW9dVlYohu8+6jCAxMnq1eiNKiJCWRz168Hoc2LsD+f+fjzsMXePX2gywvlM8OFUoXgbvnZ8xeuj7Rj9u6sT2mjxuIiXOW4/aD5xjUozW2rpiJ2m2GwsPrc4zzjY2MsP3vWXKq7eDx8/DR1UMuqeDj65eUl0VElHJUbQY0iMolvXwIOL/rm3dZOudn9OvaCvXaD8Wte09k2T+b9+H2g6dceoAoJQRQ751dZLL4yAGdUL9WJRn4CB8+umHN1kP4a93uWAOebxncqy227j2BHQfOyNsTZq9Ag9pV0K1tI/z17+4Y53dt21CufN66z3i5F5Pg5OyalJdERJRy1GgJ1O2svH5xP+CwN9bTTDNp9jZltzSHWZbMaNe8njqAcv7kJi9ElEZXIhe9Sa+u7pY9ScfPXVWXL5k1GtmyZEa/MXNi3GfTX9Ph/dlXJq6Ldak8vHyw79h5ubVMXJtXGioUMBSzWHTxGgwNYWNmBldfX4RyZdpkxbbXH7a9doXXbI1w+3byuqHDXhheORTjHLHMwLql09Gkfg3Ua9IPT985ybYvVbwQLM2zwuHqHT3UPH3h+z7ttn1CVzf/7pXIrSyyIY+dcjHN9x9ck9TzJFhaZJWzRNw8vDTK3T28UTh/7ljvk88uJ2pVKYt9R/9DzxEzUCCPLX6b/COMjQzxx6rtsd7HzMQEFqam0CXxiyX9YNvrD9v++4hvst6Vm8G7YmN52+LaYZg/cQDMzeXtTJkyauQsFcmfR/Y21atTFZ77lf93vT95yIvY5oKSB9/3aa/tHT08dBtA2Vctiymj+qJ08YIa5Q+fvsZvf26AQxKTyBNDYaCAh+dnjJ+1XPY4iY2Nc9pY4cc+7eMMoHyDghAQopsZgvxGoj9se/1h22sneAr/oSMiooInw3Pb4X/jBPwBuWnv6j9+QYmiBVG0WluER7Xx6GmLEBQYDJf3n9j2esD3vf6klLZPUgDVtF51rF4wEW6e3lixfi9ev1MlkedGh5b1sPmvXzHkf/M1huK+RezwLfKYrK0sNMqzW5nL7QRi4+rmhbCwMI3huheOTshhbSmHBEPDYu5KHh4Zqf4HpCviF8rNJfWDba8/bPvvUL8bUK2Z8vqpzTB57AD/qLb84OqOcqWLypl0FcqVwOUbyi+n/125rd5UlW2vP2z79Nv2SQqgJozohWev3qJtv4nwDwjUOPbn2p1ydp44JzEBlAh27j95KXu2VPcTWw3YVy2H9duPxHqfG/ceo12zOvK8yKgVeQvmy4VPrh6xBk9ERClOwx5AlSbyqt39Y9gzqztCQzujduuBsiw0NEyuEP789Ts4Rs14JiL9S1I2dV67HNh+4HSM4Em1n9K2/aeQN1eORD/u6k370b19E3RqVR+FC+TGvCnD5CwT8VzC0lljMGnkl60HNu48BvOsZpj1v0EomDcXGtSujJ8GdML6nUeT8rKIiHQjqxWQI99Xl/xA2xHK4CkyAji6DhE3T6FyuRKoWqEUbHNkV9/9xLkrDJ6IUpgk9UC9euMkp8vGxdrKXD2slxgHT16USenjf+wB6+wWePTsNXoMmy7XeRLsbK0REW3vJ2cXd3QfNg2//jwQp3ctkz1PYhkFMQuPiCjFBE9D5gNGGeI+R6QhOD7ARx8PdB40CQ7X7sSZukBEqXkhzSXrsWLeeNx9+Bwn/rsWIz+qZ4em+HHC70mq0L87jshLbDoOnByj7Nb9Z2jVe3ySnouISOcyZYk/eBIMjZTn+Xhg75GzyVUzIkruAKp/15Yy6XvNoklwcfOE4/uPslwsIyASuF+/dcaAbq3kRUXkKMW2lhMRERFRugigShTNDzGS9uGTu7ydJyrfKSw8QpaJRd6KF8mvcR9VkjcRUVpnYpJRzhBW7ZBARGlPkgKoas2Vs0OIiEjTtHGDMHpwN/Sc8AeOfowAKtTTd5WISAe+eyVyIqL0LEtmU/j5ByhvGGXA0ww50e2cF06W6ASUUOi7ekSkIwygiIiSaM3iqejatjHK9v4Vr83yA8WrYicyAW/FGuIK4P1z4N0ToFYbfVeViLSMARQRUQKJved8/URwJDars8GxkByYvd0Jb+z7fDnJ2w14eBF4eAnwclWu+cQAiijNYQBFRPQNmU0zYduqOahQuQKK9f8dAYUrAbmLYo9coi4UCA4EnlxTBk2i10nubhcl0A8IC4l/KQNxXJxHRKkGAygiovh6mxQG8Lctgr88rHF+ixOC63b7svjlm4fAg4vA89vKICg2Ph7AqgnKdZ7iIoIncR4Rpa0ASqz3tHrzAVy/81jerlaxFF44vpdrQRERpSX58thi47IZCDCzQbO5O4ES1YEs5jjpEtWr5OakDJoeXQb8lLskfJMIjhggEaW/vfCa1K0mt1FR2f3PHNSpXkGX9SIiSjZZzTIrr2TOBqfcFTHseRY0O+0DVGkqgyf4+wA3TgDrpgJrJgPXjiY8eCKi9NsD9cnVE2WKFcK+o+flbYVCwYUxiSjVq1KhFJYvmIJDjv6YdfIJULAMwg0M8cgrDAgPUw7NiYTw1w+ACC6KSUSJDKAOnLiAob3boVVje3z2VSY6Tv6pD0b07xjnfUR81ajLTwl5eCKiZCG+/Il1m2Ruk10RPC5YB40cIvA5JCNQuLzypA8vgQcOwJPrQFDUjDsioqQEUL/9uVHud1erchlYWZrL3qeAwCB4ffZNyN2JiPSuTbM6GDdhNH6/8BqHnSMAixyQ4VFIBBS+noi876CcRef5Sd9VJaK0EkBFRERgy54T8iI43T6ApWt2Yt8x5ZAeEVFKY2xsBCMjQwRGGADFquC//E1x4IT40mcNWEC59MCzGzIhPPLdM82lB4iIdLIXXouBnIFHRCnW0H6dULd7b/x6/DGeKqwA4wz4LA5ERsLg3WNE3HMAnt8EQuNYeoCISBcB1IePbvJnnlw5UN++EnJHzdBz+uiGsxdv4b2zS1IelohIU1arBK2fJPKaAoOCEW6REyhTGxut6+Hvs15ABlvlee4f1EsPRPh6JVv1iSjtSvJCmtPG9sfA7q1hYKC5WWZERCTWbDmImYvXaaN+RJSeg6ch87+5gvcI42fIU70OfjvzHJ9Ns8tisbWvcWgQwu5fVOY2fXJMvnoTUbqQpABqSK+2GNyzDQ6fvoxVG/fJRTWFIgXyyPJBPVvjo5sH/tl8QNv1JaL0QvQ8xRc8CUYZsDyyFCKveAIieBJLD7y8K3ubQl/d49IDRJSyAqge7Zvg5PnrGPq/+Rrldx4+x48TFyBjxgzo1aEpAygi0rlIGCDL54/wu3pSuR8d95QjopQaQOXOZYM1Ww/Gefy/y7dRt2bF76kXEVHC7PkTfiIhnIgopW3l8jUPz88oWbRAnMfFMU8vOeeFiCjRsmXNghW/T0zYyZ+Vk1qIiFJ8AHXo1EV0b9cYI/p1RCaTjOpycX143w7y2METF7VZTyJK4yzMs6qvf/bxg7WlWKyJiCgNDeEtWLEFpYoVxMSRvfDzsO5wcfOU5TmsLWFkaIhLNx5gwcot2q4rEaVBhQvkwdaVs2FlmQ2Fq7VT77M5fu1RoERDfVePiEh7AZRYb6XLkF/QpG411Ksl1oGykeXnLt3G2Ys3ZYI5EVF8vU1e3srFeD98ckXRQvlkD3ap4gXx8MkroFBZvClSR9/VJCLS/jpQwon/rskLEVFC2Fcrj7WLp8LxnTOadh0pywIDg9G+/3jcf/wC7h7eQOEKQPuRgKGRWFgOMIgn0yAshLPuiCj1BVC60rdLc/zYpz2srSzw+Lkjfpm/Cncfvvjm/do0qY2V8/+H4+euov+YOclSVyKKm6GhIbJkziRzmoQPn9xkb1MOayuZKK4qP+twQ3mHopWAtsOVwZNYkuDcTsDE9JsrkRMRpYokcl1q3dge08cNxB+rtqFJt9EygNq6YiasLLJ9c2mFqWP74+qth8lWVyKKW5e2jfH+zhHMmTRMXeb49gNa9BgFu3LN1MGTWvEqQLsRyuDp8RXgwErlDDuXt3FfGDwRkZ6kuABqcK+22Lr3BHYcOIMXr99jwuwVMueqW9tGcd7HwMAAy38bh0Urt+LtB+7DR6QPJiYZkSlTRo3lTmxzZEejOtWgUHzZ8uno6UvwDwjUvHPJ6kCbYYCBIfDwEnBwFRAZkZzVJyJKvQGUsZERypYoDIdr99RlYkaOw7W7qFS2WJz3GzukK9w9P2Pb/lPJVFMiim5gv454e+cIBvVspy4743AdrXuPRek6XdQz62JVqibQaqgyeLp/ATi8msETEaV4KSoHytIiK4yMDOHmoblbukgsLZw/d6z3qVq+JLq2bYTGXUYl6DkMFQoYxpeU+h2MDQ01flLyYdsnL/NsZvDx9UdERIRs86DAIFnWpE51/L1ul/q8E6cvQfQ9ZYjj9xJe2h7hzfoBCgMY3DsPwxMboNDR32daxPe9/rDt027bh4SHp74AKrEym2bCn3PGYvzMv+AZNSX6W8xMTGBhGk9SqhbYmJnp9PEpbmx73Zs+ZRi6dmyGoT/NwHkH5RYqB46cw+s3Trhy7R7szM0T9Dg+xavD44cu8rrZo4uwunYACvP4cx0pdnzf6w/bPu21vaOHh24DqMIFcqNrm4bIa5dTzqaJluIgiR57sVZUYnh6+SAsLFzOvosuu5U53Nw1e6WE/HlyIq9dDmxYOlVdZmCgrMi7m/tRu+1QvHX6pHEf36AgBISEQBdENCx+oa6+vghNYARL2sG2153sluZw9/RW3/YJCJQbhpevWApbD51Wfwvcf8ohwW0fXr4ewqOCJ4NbpxB0ZiucdVT/tIzve/1h2+tPSmn7JAVQHVrUw+IZoxAaFobXbz/A++vZNECMgCohxOPdf/IS9lXLyqUIlI+jgH3Vcli//UiM8186OqFeh+EaZRNG9JI9U9N+Xw3nT+4x7hMeGYlwHTe4+IUmtAuQtIttrz1icsa+9QvRvEFNlKzdGS9ev5Pli1ZtwYZdR3Dr3pOktX2lRkDjXsrr148j4sxWMOPp+/B9rz9s+/Tb9kkKoMYN7YaHz16j5/BfEzx0llCrN+3HklljcO/xS9x5+ByDerSBaSYTbD9wWh5fOmsMPrl6YO6yjQgOCcWzV8p/6iqfff3lz6/LiejbRG+vXMwSYg3LCLk1k5GRERrUrqIOoN6+/ygvSVKlCdCwh/L61SPAuR1aqzsRUYoPoMSed39v3Kf14Ek4ePKiXPNp/I89YJ3dAo+evUaPYdPVQwh2ttaIiG9GDxElmtiHbv/6RShXqghsyzRVLzMwYfafGD11kTp4+i7VmgP1uyqvXzoIXNj9/Y9JRJSaAqgnL97IIEpX/t1xRF5i03Hg5HjvO2baEh3ViihtEV9QVLmFYs0mm+wWcj+6mlXK4tR55RZNcl86bajRCqjbSXn94j7AYZ92HpeISE+SNF94xqK1cmHLyuWKa79GRKRTJYsVxIPzO+BwYI1Gee+R05GnQgt18KQ1tdp8CZ4u7GHwRETptwdqeN8O8PULwL518/D89Xu5v1VEuGYaqFg4rx/3oyNKEcngYo01VW7TO6dPyJ/HFgYKAxQpmFc9PHdNF9sg1W4P2LdVXhf72l09rP3nICJKLQFUiaL55TIFHz65yxlvRQvmjXFOvCsPE1GyaPhDNaxdPBUPnrxEy56jZZmffwBa9hyDOw+eysUwdaZOJ6BmK+X1M9uA68d091xERKkhgKrWfKD2a0JE3y1DBmM5a9X7s6+8/e7DJ+TNnVPmNokvO6rk8POXb+m2IvW6AtWbK6+f3gLcOKHb5yMiSmbcM4EojejTpSWc7x3DtHGD1GXPX71Fky4jkKdii5gb+OqKWKZAFTyd2MDgiYjSpO/ayqV6pdJoWLsyctvayNtOH11x2uEmruoil4KINJhlyYyw8DAEBgbL267uXrCyNEe9WpU0zjv5n3JRWl2LFLveiQUyxUKZwrF/gbvnkuW5iYhSRQBlbGSEFfPGo2m9anKlcNXildnMMmNIr7Y4dvYqhk1aILdlISLtmzp2ICaM7IOff12CvzfskWUnzl1B064jtT+LLoHBU7gInsrXAyIjgKPrgPsXkr0eREQpeghv7NBuaFa/OlZt2o/yDfugVJ3u8lKuQW+5wGbzBjUwZnDUgnlEpJU1m8SXFRUfP3+Z01SvVmV1mVg5XARR4mdyB0/uP3RGhCp4OrKGwRMRpXlJCqDaNauDXYfOYvaS9RqbjHp4fcacpRuw6/A5dGxZT5v1JEq31iyeig93j6F+7Srqso07j6Bmi/7oMniSXusmNr0Mb9YffsWriwgOOLQKeHBRv3UiIkqpAZRYsfj2g2dxHr/z4BmsrSy+p15E6VZOGyuN20FBwTA2NkLdml9ym7y8fXDl5n3olcIAaDkYEWXsgYhwGB5eBTy6ot86ERGl5ADqo4s7alYuE+fxGpVKy3OIKOGMjAxxatdyON09ivx5c6nLF67cjFI/dMbUeSuRYhgYAq2HAqVrAeFhsDmzCYZPr+u7VkREKTuAEsN3rRrbY96UYSiUz06udCzyM8T1uZN/RMtGtbDz4Bnt15YojbHNkV19XUy6iIiIlH9LdWpUVJe/eeeMx89eI0UFT21+BEpWl8GT0YEVyOx4T9+1IiJK+bPw/ly7C/ny2KJnhybo0b6x/KcvGBgo5D9/EWCJc4go7mG6I1uWomA+O+Qq11S9FMHY6Yvh4+uH9x9ckCKJ4KntcKBYZSAsFNi3DAaODwBzc33XjIgo5QdQYpbPmGlLsHrTfjSwrww7W2tZ/uGjG85cvIknL95ou55EaSJo+uTqIa+7uHnCPFsWuUJ45XIl4XD1jix/9PQVUixDI6DdSKBIBSAsBNjzJ/D6PmBoqO+aERGlroU0RaDEYIkofuVLF8OuNfMQFByCMnW6qPeK7DpkCl69cYKn12ekeIbGQIefgELlgFARPC0BHLlgLhGlX9zKhUgHyeA21pbq26/fOiFXDmvktcshh75Vbtx5lDqCJyNjoNPoqOApGNj1B4MnIkr3EtQD5XT7gMxzKlS9I0LDwuRt8Q06PuJw3spttVVPolShRSN7rPnjF1y5+QDt+42XZT6+/mjSdQRuP3iKgIAgpCrGGYCOY4D8pYCQIGDnIuB93EuYEBGlFwkKoBav3i4DprDwcI3bROmdqakJMhgbw/uzr7zt+M4ZOW2yo0r5ksiYMQOCg0Nk+cVrd5HqGGcEOo8D8hYHggOVwZPTc33Xiogo9QRQi/7eFu9tovRoaJ8OmD91JFau34OJs5fJMrHcQIOOP+LCldupey/IDCZA55+BPEWVwdOOBcCHl/quFRFR6s6BEvvcFSuUN87jRQvl5V54lOZYWmRDpkwZ1bc/urojq1kW2Fcrr3HeWYcbqTt4ypgJ6Po/ZfAU5A9sm8/giYhIGwHUuKHdULJogTiPFy+UF2OHMICitGPuLyPgfO8Yurdvqi47cuoi6rUfgtqtByLNyGiqDJ7sCgOBfsrg6WMKWsSTiCgtz8Izz2aGkNAwXTw0UbKws7XRuO3p5SNzmmpVKacuE71M/126lXbyAU0yA90mALkKAQG+wNZ5wCcuU0JE9F3rQFWrWEpj/7tm9Wsgf7Qp2SpZzTKjdZPaePrybUIfmihF2bF6Ljq2aoB67YfKXCZh3bYDOH3hmtwoO03KlEUZPOXIBwT4AFvnA27v9V0rIqLUH0DVqlIGY4d0k9fFN+7mDWrIS2yev36PX+at0l4tiXQoj10Oja1TvH185f6OtaqWUwdQHp6f5SVNMjVTBk82eQH/z8qeJ/cP+q4VEVGKpoBNpQSNP5hkzCC3nRB73d0/uwkT5qzA0dOXNc4RgVVgUDCCQ0KRHmUwNISduTk+eHsjJGrJB0q5bS+G5M7uWYlqFUsjX6VW+PDRVZaLxS6NDA3lKuFpXuZsyuDJOjfg560MnjycE/UQfN/rjy7bvlqFEqhVubS8nkYGqbVKIf6HGBshODSM7ZOK2l7c19vHDxv3nPzuyT4J7oES21CIi1CtxUD5bVx1myg19jaJNZpCQsLkIrGit2nngVOy/O37j0gXspgD3ScCVrkAX09l8OT5Sd+1ohSgU8u68sNl6bo9CA+P0Hd1UiTxQWxsaIjQ8HAGUKms7csUL4jeHRpj3Y5jyZ9EbqAwQO1qX5Jpv9bohyrInUszCTcx+nZpjmtH1+D1tT04vGkhypcuEue53ds3xr518/D4wjZ52fH3rHjPp/QbOD26sBN3Tm+RPU8qwyfNR54KLdTBU7phZgH0mKwMnj57AFt+Y/BEanly2WDfcQcGT5QmPXj6GuZZs3z34yQpgJo2tj8GdG8V5/G+XVpgyk99klSh1o3tMX3cQPyxahuadBuNx88dsXXFTFhZZIv1fJHYvv/4BXQaNBmte4+Hs4s7tq2ciZw2X/Yio/RH5DDlzpVDffvDRzdkyWwKE5OMKFfqS4AtFr50cfNAupLVShk8WeYEvN2ALXMAL+XwJZGQZmaWEsVBG+/wJAVQlcoWw4WrcW9NcfH6PVStWCpJFRrcqy227j2BHQfO4MXr95gwe4XMq+rWtlGs54+YvAgbdh7Fo2eOePnGCeNmLJM9ZPZV4+4ho7StWqXSeHPzIA5u/ENdFhERgfb9x8O2TFNcv/0I6Va27MrgySKHMmgSPU+f3fVdKyKiVCdJAVS2rFng5x8Y53H/gCBYZDNL9OMaGxmhbInCcLh2T+ObkMO1uzJoSwiR6G5kZAjvz36Jfn5KncSQXA7rLz2OIvC2yW6JvHY5YJsju7r81r0n8PXzR7plbqMMnsytlcN1InjySWe9b0RJJBaQvndmI5zvHkLTetV19jy6fvxvqVG5tKyDWJJI6Ny6AZ44bNNoh1M7liKl+/p16EKCk8ijc/7khirlS2DjrmNxrhn10SXx32otLbLK4MfNw0uj3N3DG4Xz507QY0wZ3Rcubp4y6IqNoUIBQwOdrB8qk9qi/yTda9eiPlb8PhHnLt7A+AkLZNv7+fihcadhcs0mkSguZiqlF5FmlogUyxJ8zcwSYY37AFmyAZ4fYbz9dygCPgNaaBu+7/VHV22viLqkNotnjpYf+EJIaKgcut99+ByWrd35XflchQvkxrih3dF/zBzcvv8Un3385Ix0Qf5MwpDn2KHdZKDUuMuoWI/rq/0VX70HDp1wwFmHmzHqo9BR0LN7zVyUqN0VPr5xf9lNSNt//TpiOx7XZ0NCZ7QmKYASOUejB3XB3YcvsG77YfV4ucg76de1hcxj+nPNLiS3Ef06ok2T2ug4cHKcSymYmZjAwtRUp/WwMUt87xsljFkWUxgYGuLzZ19529fDW+5RV6lsCRl8q9r+/cv3yJ7JFBCXdCIsszmcukxGpJFx3CdFRsL23DaYiL98c3OtPj/f9/qj7bYXU8RTY0BsoFDI3QHGz1iGDMbGqGdfCTMnDEZkRARW/Lsn8Y9nYCA/3wrls5O3z164oT5mFPVFXPUzsQwVBlBAEWs7i8fUV/sbGUQF5QaGsg7hYeHw8fFT1ye+esc3uhQaFpbo5/72+QZJfizxHhdLgMTG0cNDdwHUsrW7ULV8ScwYPxA/DeyEV2+Ui+4Vym8nk70v33yApWt2JPpxxXYZYuqstZWFRnl2K3O4uWv2Sn1taO92GN6/A7oMmYonL+LefsI3KAgBIbpZfkH8ksQ/MldfXzm9krTrp0FdMWPij1i6ait+/V25UOuHq7fRoN0Q3Lj9CNkzZ07XbR+RMVv8wZOgUMDNzwcG3t5ae16+7/VHV20v1tdJjb/LiMhIBIWEwtlV+QH4744jaFS3GhrUroKla3Yig7ERJozojTbNfkA2s8xyx4zflq7HlZsP5fmi9+rX8QMx6pfFmPxTHxTMZ4e9R/9T92q9ubVf/rQr30r2fvRo3xgDe7SRs3ydnF2xbtshmZOrYmtjhV/G9EOdmhWRMYOxTC+YMvdvFCmYB6Oj9otVPeaYaUuw8+AZeT0sIkK2/87Vs2MsTC1Gam6dXI9ew3/Fxev345wJP3pINxQvnA8BAUG4ducRBo79TR7r0KKenAQmPq8DAoNx6cZ9TP/9H3h4KRcKDotQ/t5DI8JlHVRtUrK2ciHt8MgIRCISXdo2wqhBnWGRLStOO9zA+JnL4OsXoO4JFENn9x69QJ/OzeXWbjVaDIz3ucXM/e2rZ8v73z+/Rf4U7SHaRbT18H4d0KNDU1hbmcPxnTOWrdmJQ6cuqTtw6ttXwozxg2Tahhh92HXorMbriO09LtZP+x5JCqBEY3T9cRo6t66P5vVrIl/unLJc9EgdOXNZVjwpszhEhHr/yUvYVy2L4+euyjLRcCIhfP32I3Heb1jf9vhpQGd0HzYd9x/Hv2t8eGQkwnX8j0H8srig4PfLYW2Fz75+CAoKlrffOrvANJMJKpUvqdG+Zy/fUnfFpuu2T+DrDhPn6aCN0nXb65m221789471P3ik6jkMZDCuLIuIOlsBKAy+ca44LyJh5yax3tF/CuL/h8jJFWWzJw1F0YJ58OOE32Wqh9iSbPPyGWjQaQQc332U54g8WvFh/fPMZfDy9oWLu6fsFFgyczTKNeilfvx2zX7A2KHdZUAkpsWXLl4QC6aNgH9gkPwMFP+rdq+di0+uHug3ajZcPbxQpkQhKAwUOHDCAcUK5UPdWhXRZcgv8jFF8BG93uL61n2nMHviEMxYtFa9v2z7FvXwydUTDnEETw1qV8aaP6bgz7U78dMvf8igsb59ZfVji57631dskQsFZ7c0x68/D8DiWaPRa8SMGG0Y/X0QvW5iG7eWje3RZ9QsObt50a8/4bfJP8pJXapzxee4eE0iVlCVxffcHz65Y8DY37D2j8mwbz0Evv4Bcq1Jcb+RAzqhQ/O6mDB7uQyealQqjSWzxsDVwxtXbj1ErhzZ8c+iyVi/4wi27DmBsqUKY/rYARqvI7b3yvf+zSQpgJJPHhkpZ8qJizat3rRfNsy9xy9x5+FzDOrRRr4Rtx84LY8vnTVGviHnLtsobw/v2wE/D+uB4ZMW4r2zi4xOVYnsAYFBWq0bJXCKvNhXLS6BfglKXF4652cM69sRA8bMwsadyuD5wPHzqN68L67dUn5bTN8UyoUwxaw61SVnfn1XitI6t6jc0uxlAUVUb2eAC+DvDJhkB7Lmi3au+ICPAKxKA4YZlWWBroCfE5DREshW4Mu57g+ByDDAsiRglElr1RXrFYren3+3H4ZdTmt0ad0QVZr1l8GT8PfGfahXsyK6tGmIecs2yTIx9Dfpt5V4/PzLSIYqH8fN40uPhciJmrP4Xxw7e0V+GIvPHxGc9erYVAZQ7ZrXkSMyzXuMlStfC2+iLdLrHxgov8xHf8yvHT1zWQZQTepVx6GTF2VZ51YNsPOg8vMwNqMGdsaBExewcOVWdVn016L6LBXefXDBL/NX4/jWxfJzNqGfmRkzZMCoqX/IQE4QPWSblk2TgZ7q9Ygepp9nLNMYuvvWc4tttAR3r8/qNhcB4E8DOslA89Z95V6kYjFkkWvds2NTGUD17twMb50+YeYf6+TxV28/oETh/BjRvyN0KckBlK4cPHlRvunG/9gD1tkt8OjZa/QYNh3unspfip2tteymVRENJ7pG1yyapPE4i/7eikV/f5k5QMkUPA2ZDxh9WagyhrAQYNWEGEFUgXx2cHz7Zf81ESQbGRmhSvlS6gAqJCQ0/QRPClWAZK0ZJKkuoq2/NVxHlA41rF0FLy7vlP8/RE7UvuPnZTBRs0oZ2QNy8cDfGueLgMkrKqdSEPmz0QOO2IheqgJ5c2H+tBGY+8swdbmhoaF6pm+pYgXx8OlrdfCUFKIue46cQ9c2DWUAVaZ4IRQvnBd9R8+K8z6lihbElr0n4jwuesF+HtodJYsWQLasmWWel+qzVQwxJsSHT27q4Em4df+pfO2F8udWB1BPX76JkfeUlOfOnzeXshPlb83XbGxsJNtXKFIgT4yN3kWddC3JAZTo6enWrrH8hWbNYiq7JaMTMY6qazKxxLi1uMRGJIhHV635wCQ9B+mA6HmKL3gSxHFxXrQA6siWpWjesBZqtuiPKzeV3dL/bN6HfUfP4Wk8+WyppTctzgDJzFL5HObZYwZKotzwG3+eIlfBxxP47KZcy0n8s6pYP2n1IUoI6/JRV6INv5nmAEzFzhNfDb1Zl415biYbIFP2mOdmL/09K+touHzzPibOWYnQ0DB8cvNQz77LnMlE5tg27TYG4RGaM/L8A74syxMUrEwZiE9mU2Uv2cTZy3Hz3lONISLV86lSD77X1r0n5bIBIp+qS5sGMm9IzC6MS2A89ReB37YVM/HfldsYPnmhzDsWwYtYfFr09GhTQGCwVp5b/N6EXiNnyi/WiHr3iATyAC21cVIlqcVKFMmP3Wt+kxsMv377QSaqiUQ3kZSX08YKb5w+yaUOiGJjZ2uDDy5v1bdd3T1lV7ZYfFUVQImlK8QlNfamSSK/Q2yXIoIhse6SDIqiB0iW3w6QwsOiAiT3qEtUoKS6+HpF5Z9EyZGPARTpliKWmVHRc5m+ea746Iut3FCrH9zRh8pURG+F6IGyssyG63cef9dziBGRj64eyGuXUy6TEFuOjZjM1L1dY7llSGy9UCKnSdUDEx+R6C5SWnp0aIK2zepoJJQjjucVecOxpdeI5RhEEvpvSzfIXTuEsiULI7HEcGgOa0v1UGjFMsXk//D4NmBPyHOLoFeIvtSQiC1ELpR4zqtRIxDR98ITXji+R+M61TQeS9QpRQZQk0f1kZn9Yv0KsUr4/bObMO33f2Rk3LJRLcybPAwjJi/Ufm0pTXBtMRKK+oGIDA0GwkIx+FkkBv96FqFBRYBuE4HwUFmuvIREux7ttjgnNER9bkREOAJNMiDC1BsICYp5rur+2u5NK1Ba2RMUowfJEoiaRht/gOQBeLsDPtGCJO+o634iQOKWGkTa8PqdsxwO+3P2WJmrIwIqK8usMtgQQccZh5uJerxFK7di1oTBMjg6d+kWMmQwRrlShZHNLAtWbz6A/ccuyNyddYunyJxdEWyULl5Ibh0lcnnErD2x2G+pYgXw0cUDfv4B6kTxr23ddxJzJg6VeUIi5yo+Yhu0natmy5wgseSQkaEhGthXxvL1e+TQmxgW7N+tpVzHUXR+jBncBYkVHBKCpbNGy5wjkUQ+e8JgOcQYXz5XQp7b6aOr3DWi4Q9VcObiTQQFhcjeQZGrNuPngTAwUMjgN2uWzKhesZScZLTz0Fls2nUcQ3q1w9Qx/WSPnQjMVDMnU1wAVaVcCazYsFc2iGpDPvHChMOnLqFqhZKYOro/OgzUzEuitEoB5CsBVG2aoLNDRadJxkzKi7ithRqIfzsJ2go3RkAWS2AmfiY0v6i5cqZHnAFS9B4j2YPk8eW6n7d2AyQxpChex7d6zcR5ROnQmOlL5RqG08cNkPulimGk2w+e4XS09Z0Satu+kzIvc3CvNnKpAhHcPH3xFv9sOSCPi/wfMQNNzAbbtGy67P0SvSmT566Ux4+cviRnAe765zf5OTo62jIGXxPB2Mzxg3Dg+IU41zhUEUsyDB4/XwYnw/t1hJ9fAK5GbV8lXq9YFmDiyN7o360VHj59JYOgDX8qZ8ollOjhO3rmCjb99ausu1jGQCTexychzy3yqkS+mlhCYvGMUdh1+Jy8z+/LN8ulDkb274S8uXPIBPNHT1/LpSkEEYsMGjdXLrfQr2tL3H34HPP+2ojFM0ZDlxSwqZTo/+DPL+3ArwvXyqhYLDPw5vpejJiySD1LQHRbijWiitTsjPRETKUXC3OJtSXSxXRusZ9aGXugtD2QzSrBd8tyZAX8nN4AhsYiE1D5U3zoi6BF4xKtTJ6bIercmMcVRhlglNEEoWI4Ifq54vHjGmLQBrEZr9enL71G0Yfa/MS6KpFpJ28rDunufZ+C6KrtxwzqhMX/JP9iyKlJ9GEkXf+VizWSrhxajeY9xuHB01dI7xRaaHttvMeT1AMlph+KhcNUyxm8c3aR00VVAVTlcsXxOZ5l2CkVE71GxasCZWoDeYp+KQ8KQKZPLxGYX5U4Gjc/l4/Kvdi0yDi+DxIxlBZbUBZfYGZhI2YofPuJ9/4JRMvn0jsRHHF/O6I0QfRaiTWsJgzvKXvKGDylLEkKoC5cuStzneb/pVw3Q4xnTh/bH/nscsokwZqVS2PVJuXqqpQGiMTPfCWBsrWBopUA46g1XcRMFscHKBHkhOOz++O/Z5nQ5zZSHpGjFCIuiVgXTCRkJySAIiLSEbHn7J41c2Vy9qCf5+m7OqSNAEps0yLW1hDRsZgW+s/mAzA1yYgWDWvKKZxL/tmhl73wSMsscyqH58rUUg4NRVF4OCPL61vwvXpK5vG4Z7eAreUYlMgdBlz/FH/+EHNwiIgSROQz5SrfSt/VIG0GUGINjeev3sngSUUkc6kSuigVy2gKlIgaostd5Et5oD/w+AqaZA/DzpmDcfBECHqdVgbJYp/CWq0G4M6Dp4CpebLn4BAREaX4AEosePX4/DaZ4b5i/V7d1IqSf4guf2llQrgcoouaxRURAeP3T5Dh6VX437ssZ6h5lC+JrGZZULZkEbmGiZhyKty48yht5eBwRhsREWkzgBLrVIhNEb81lZJSAUvbqFl0YojO8ku5mxNw3wE/1cyHBfP7YM4ST8y8fV4eunn3cfrYj04EgWKRTPamERGRtobwxFoVnVrWx8adx2LsdUMpnIkYoqumHKKzi7YKbKAfsry9h+BbZxH67oUscs/TNGpxuGhDeUDaD55U0kpvGhERpYwASuxP1rRedZzbu1wGU2IXarFi6Ne+tWIqJeMQnVgxWwRNRSt+GZYSs9Ne3QceOODvoc0xaMIA9Bz+BtuiAqi9R86hfP3uuPfouX7rT0RElBYCqBXzxquv/29Yj1jPEetD5anUNuk1o+9nlStqiK6mcuNaFdf3sHZ+CLcLRwF/sdgj8PFDRZnTVK5kUWyL2slbbIbJ4ImIiEhLAVTHQVOScjdKtiG66so1m3IV0szXeXQZeHAR51ZMQt1a41C58V3cuqcMoFas34WNu47A8e0H/dWdiOg7iE3uHz17jekL1ui7KpQOJDiAmjiyFw4cd5CbLqp2RKYUQmxVUjBqiK5IxS/rMIkhupf3kMv9GZwvnlLuzSb3DXKVu15XKlsCt+49US9FIC5ERKlxSyGiFBtAjejXEc9evpMBlCCWl79/dhO6Dp2GSzfu67KOFJfsdl9m0WUx/1Lu+k7OojN7dx/X9yxDwXxdYFf+CtyjdsqeMncFxkz7gwETEekmeBoy/9tLgIhZrgyiKBX7rl1WxUbClMxMMgMVGwB9fwUGzQWqt1AGTwE+UNw8iTwnlgNrfwFunICvy0f4+PkjJDQUFcsUVz/E2/cfGTwRkW6Inqf4gidBHI+vh0oLspllxtJZY/D4wja8urIbm//6FQXy2qqPPzi7We6eoXJqx1LcObVBfbtq+ZJwvL4XmUyitq4i0kYOFOljiK5M1BBdhS9DdGJI7tU92dtU3MAHp3f8KYPavBVbIjxqQ93eI6bhw0c3+PkH6Pc1EFHqp1pkNz7xbeX09XnxPV5ozJndibFk5mgUyJsLfUfNkv//pozqi01//Yq67YfJXTSu3n6EmpXL4MjpyzLYKlwgD4KCg1E4f268fOOE6pVL496jFwgMCv6uelDaxQAqJbPOrRyiK1VTc4ju01sYPb0CW/cXeP9CueTAK2MjZDA2lgFU0UJ58eS5oyx/9vKtvmpPRGnNz1pMzu49Lf7jc3sn+aFFT1OTetXRus943Lz3VJaNmLwIN4//K5fgOXzqEq7cfICeHZvKY9UrlcbDp6/g5uGNGpVLywCqZuXSuMJ8X9JWAJU7lw3KFFfO7DLLYip/igjfx9c/1vMfPH2VmIdPuxKTUCnOK1lDGTjZFvhyjr+PehZdw+K22Ll+rpxtUrv1QHlYJIU36PSjDJhCuEo8EaVjRQrkkf8Tbz/4sgyL12dfvHrrJI8JIjia+b9BsLTIKgMosXGv2GVD9Ept238KlcuV4HZlpL0ASqz59PW6T3MnD41xnugF4TpQiUyoPL4BKFxeOURnaPRliO7lXZi+vIFsbq/x8aOLLH4IfxnA5ra1QbasWfDZR7kf24PHL5PlJRFROrVQ+YUtXjZ5v927JGycqZzwoidiQpT3Zz/UqFRaXub9tQluHl4Y3rcDypcqAiMjQ9y4q5ylTPRdAdSY6UsTeiolJaGy5aAvtz86Ag8vAo+uYkD7+lh2cAG27z+J/qNnysOfXD1QtWkfOT6v2syXiEjnEpKXFJbAHnBx3nfmOcXlheN7GBsboWKZouohPDFzvFC+3Hj++kvQdu3OIzSpW12mPVy/81jmO4ntq8TQ3v3HL5n/RNoJoHYdOpvQUykpAn2B+xeR3fkhwj+9hZe3jyx+8vwNMmUyQYmi0YbzANx58ExPFSUiStkc333E8XNXsWDaSEyYtRx+AQGY/FNffHTzwIn/rqnPu3zzAaaPHYB7j18gIDBIll27/Qjtm9XFyg0cviMdLmNAWrRjEWZXs8Cns2swtE8HdfHlG/dQsWEP1GjeT6/VIyJKEJHTKdIS4iOOi/N0aMy0JbIXacOyqTi0YaHcErTXiF/lDDwVsSi0GKoT+U/RgypRJn4SxYez8FKKiHC8eP0OhoaGKFGEvU1ElEqJCTFikUw9rETeceBk9fXPvv4YNXVxvOc/euaIXOVbaZSt2XJQXoi+hQFUCrLz4ClcvfWASw8QUeomgiOuMk5pXIocwuvbpTmuHV2D19f24PCmhShfuki857dsVAsX9q2U55/ZtQz17SshNQoMDGbwRERElAqkuACqdWN7TB83EH+s2oYm3Ubj8XNHbF0xE1YW2WI9v3K54lgxdzy27T+Jxl1HycTBdYunoFihvEgJFAYpromJiIjoO6W4T/fBvdpi694T2HHgDF68fo8Js1fIqaTd2jaK9fyB3Vvj3OXbWLlhH146OmHBii148OQV+nVtCX0Qa2CVilpsVIj094GxIlLvCZVERESURnOgjI2MULZEYfy1bre6TCzI6XDtLiqVLRbrfSqVLY5Vm/drlJ2/ckeu7REbQ4UChjrqFcplkx3nj6xFdisL5K/YUrkUgb83bPfOh0+EAXz9Yt+PThHgC4W/N2BoqJN6pQfGUW2n+knJh22f9tpebBPPreK//WVZ/TPyG1+SKcW1vXiEDHH83YRE7SWbqgIosaS+mD4qVoONzt3DW27wGBvr7ObyeHRiPyOb7NH2jovGzMQEFqbKbWi0LiRMbmuT1SwL6lUpj2s37kdVyAWiNrHXSER14mCcRykRbMzM9F2FdIttn3ba3sTYmAFxAhkxTSNVtr14j9vF8bnr6OGR+gKo5OAbFISAEN2sfiv+4YwcMwf3Xr6Gr3+gTp6D4m578SHi6uuL0AR+eyDtYNunvbYPCg2VK3L7Ry0uSTGJ3g/xAR4WESFHSij1tL2hoQGCw8LwwVuz8yWxUlQA5enlIxc5s7ay0CjPbmUON3fNXikVN3dveTw6aytzuLrH3jDhkZEI1+E/ece3H2TwlNAuQNIu8SHCttcPtn3aafs9Ry9gWL92WP7vPgZRcYn64BYf4AyfUlfbt25cCxdvPPjuv5kUFUCFhoXh/pOXsK9aVs6mU0Wa9lXLYf32I7He59b9p6hdtZzGwmc/VC8vy4mIKPHefnDBjgNnMbB7C5kzygAh9hyajMZGCA4NY/ukorYXMcV7Z1dcu/P9G0WnqABKWL1pP5bMGoN7j1/izsPnGNSjDUwzmWD7gdPy+NJZY+RmunOXbZS312w9iD1r5mJIr7Y443ATbZrWRtmShTF+5l96fiVERKk7iFq6do++q5FiiQRkkUMjhoHY85o+2z7FBVAHT16Uaz6N/7EHrLNb4NGz1+gxbDrcPZVDcna21oiINuYpdtoePnkhJgzviYkje8PxnTP6j5mDZ6++7LhNREREpE0K2FRi72Mai4rTI7a9/rDt9Ydtrz9se/1JKW3P+ZdEREREicQAioiIiCiRGEARERERJRJzoIiIiIgSiT1QRERERInEAIqIiIgokRhAERERESUSAygiIiKiRGIARURERJRIDKASqW+X5rh2dA1eX9uDw5sWonzpIvGe37JRLVzYt1Kef2bXMtS3r5RsdU3Pbd+9fWPsWzcPjy9sk5cdf8/65u+KtPe+V2nTpDac7x7CusVTdF7HtCqxbZ/VLDN+mzQUd05tgOP1vXA48Df/7yRT2w/s0RoO+1fi1dXduHl8HX79eSAyZjBOtvqmBdUqlsKGpVNx++R6+b+jab3q37xPjcqlcWLbEvl+v3RwFTq3bpAsdWUAlQitG9tj+riB+GPVNjTpNhqPnzti64qZcu++2FQuVxwr5o7Htv0n0bjrKBw/d1V+kBQrlDfZ657e2r5m5TLYf/wCOg2ajNa9x8PZxR3bVs5EThvLZK97emt7ldy5bDB1bH9cvfUw2eqa3tve2MgI2/+eJdt+8Ph5qN12qNxYXWzATrpt+3bN6mDyT33wx6rtqNN+GMbNWCYfQ+zRSglnmskEj547YvLcvxN0fp5cObBp2XRcunEfjbr8hDVbDmLhtJGoU6MCdI0BVCIM7tUWW/eewI4DZ/Di9XtMmL0CgUHB6Na2UaznD+zeGucu38bKDfvw0tEJC1ZswYMnr9Cva8tkr3t6a/sRkxdhw86jePTMES/fOMl/ZgYKA9hXLZfsdU9vbS8YGBhg+W/jsGjlVrz94JKs9U3Pbd+1bUOYZ80iN1S/cfcJnJxdZQD7+PmbZK97emt78YVZtPm+Y+dlu5+/ckd+iatQumiy1z01O3fpFn5fvll2OCRE705N8e6DC2b+sU5+zv674wiOnL6EwT3b6LyuDKASSHyzK1uiMByu3VOXRUZGwuHaXVQqWyzW+1QqW1wej078UYly0m3bfy2TSUYYGRnC+7OfDmua9iS17ccO6Qp3z8/Ytv9UMtU07UlK2zeuWw237j+VQ3j3zmzE2d1/YeSATjKgJd22/c17T1G2ZCH1MF9euxxoYF8ZZy7eTLZ6p0eVYvmc/e/K7WT5nDXS+TOkEZYWWeUHsJuHl0a5u4c3CufPHet9rLOby+PRuXl4wya7uU7rmtYkpe2/NmV0X7i4ecb4QyPtt33V8iXRtW0jNO4yKplqmTYlpe3z2eVErSplse/of+g5YgYK5LHFb5N/hLGRoRxaIt21veh5sjTPiv3/zocCChgbG8le8GVrdyVTrdMn6+wW8nM1OnFb5AKaZMyAoOAQnT03AyhK80b06yiTmTsOnIzgkFB9VydNy2yaCX/OGSvzbjy9ffRdnXRHYaCAh+dnjJ+1HBERETJlIKeNFX7s054BlI6JRGbR2zf5t79x+8Ez5M9ji1n/GwyXQZ5Y8s8OfVePdIABVAJ5evkgLCwc1lYWGuXZrczh5q75LUXFzd1bHo/O2socru6a0TJpv+1VhvZuh+H9O6DLkKl48oJ5ILpu+/x5csqhCzGLRsXAQCF/vru5XyY1v3X6lAw1T5/ve1c3L4SFhcngSeWFoxNyWFvKYanQsDCd1zu9tv3/hvXEniPnsHXfSXn76cu3MiF6wdQRWLpmpxwCJO0Tvw/xuRqduO3j66/T3ieBA+MJJP7x3H/yEvZVy6rLFAqFTEq+df9ZrPcRuQi1v0pa/qF6eVlOum17YVjf9hg9qAt6DPsV9x+/TKbapu+2F0mc9ToMl7NhVJeT56/j0o0H8rrzJ/dkfgXp631/495j5M9rK89TKZgvl5yFx+BJt20v8iyjB66C6nb03wdpl/g8/Xpy0A/VKyTL5ywDqERYvWk/urdvgk6t6qNwgdyYN2WY/Iax/cBpeXzprDGYFG3K6pqtB1G3ZkUM6dVWjpuPG9oNZUsWxr/bD+vxVaSPth/etwPGD+uJsb/+iffOLvIbibiI+5Du2l4MkT579U7j8tnXH/4BgfI6P8R1+77fuPMYzLOaYdb/BqFg3lxoULsyfhrQCet3HtXjq0gfbX/qwnX07tRcpguIqfXiy/L4YT1k+deBFcVNtHGpYgXkRchjl0Net8tpLW+LNhdtr7Jx13Hky50Tv4zuKz9n+3RujlaN7LF68wHoGofwEuHgyYtyDZDxP/aQiWuPnr1Gj2HT4e6pHJKzs7VGRLRuWjErY/jkhZgwvKdcC8TxnbOcXiw+SEi3bd+7czO5gN2aRZM0HmfR31ux6O9tyV7/9NT2pL+2F+uddR82TS7geHrXMtnztGbrISz/d48eX0X6aHuR5ySG6f43vKfMOxPDgCJ4mvfXJj2+itSnXKnC2LNmrvr2jJ8Hyp87Dp7BmGlLYGNtKdteRXxB7jVyhjxvQPfW+Ojijp9nLpMz3nVNAZtK/M9HRERElAgcwiMiIiJKJAZQRERERInEAIqIiIgokRhAERERESUSAygiIiKiRGIARURERJRIDKCIiIiIEokBFBGlSLvX/CYvKrlz2cD57iF0bt0AKbWOKcG1o2uw4c9pWt0kV7R7i4Y1v3nu4pmj5fNHJ+4rdmFQEb8/USZ+n0SpGQMoIi1RfTCIS9XyJWM95+bxdfK4Nj/gKH6VyxWXH+BZzTLruyoUB7H9RkoKjIkSglu5EGlZYFAw2javg+t3H8f4Jp8rp7XOdwhPq5ycXVGganuEhoUn6n6Vy5XAuKHd5VYQYod20p3xM5fB4Bsb5+4+fA4Hjl+Q+yZGD6A8vX2w8+CZZKglkXawB4pIy85evIVWDWvB0FDzz6tdszq49+gF3Dy8kJaJXel1RXzopreNWXXZntoWFhaOkND4N4wWv7/owRNRasUAikjL9h8/DwtzM/xQvYK6zNjICC0a1sK+Y+djvY9CocDAHq1xbs9yvL62B/fObMT8X4Yj21fDTk3qVsPGZdNw++R6OF7fi8uHVmP0oC4wMND8UxZ5OWd3/4UiBfNg1z9z8OrKbtw6uR7D+rZP0GsQw4xzJg5Bu+Z14LB/pazT8a2LUa1iKY3zxNCYOFc8z/K5P+PxhW04sH6++nj75nXl/V5d3Y1H57di5bzxyJUje4zn69GhiXwt4rwjmxehaoWYQ6Bx5UCJHdj//n0CHpzdLO8v6jthRC91/aaN7S+vXz+6Vj3EGj3/Rpt11GV7ioBc/K5FHcTvXuQaTRzZCxmMYx9IqFOjAk7tWCqf6789y9Gsfg2N4+ZZs2DamP44s2sZXlzeiWcXd2DzX7+iZNH8sT6eoYGBfL67pzfi5ZVdWL/klxjtFFsO1Ne+zoES5xcvnA81K5dR/37E+zevXQ55fVDPNrEOy4pjbZv+EO9zEekSh/CItOy9sytu3X8q/7mfu3RLltW3r4SsWUxx4IQDBnRvFeM+v08djs6tGmDHwdNYu/WQ/PDo17UFShcviDZ9/ye/2as+fAICgrB68wH4BwSiVtVycvd3syymmLX4X43HzJY1C7Yun4GjZy/j0MmLMoD7ZXQ/PHnxVl2v+FSvVBqtG9fG2m2HEBIaKodZxOM17zkWz1690zh39YKJcHznjHnLNspgUPhpYGf8b1gP+dxb952UO9v379oSe9fNQ+Ouo9TDad3aNsKCqSNw4+5jrNlyEHlz58T6pVPh/dkXzi7u8daxRJH82LdunmyfzXuPy7bPnzsnGv1QBfP/2oSjZ66gYD472fs3bcE/8PTykffz8PycbHXUVnsunP4TurRugEOnLmLVpv2oUKYofhrQGUUK5MGAsZqJ7AXz2mLl/P9h065j2HnorLzf6gUT0GP4r7hw9a48R7yGJvWq4fCpS3jn7AJrS3P07NgUe9bORd32w+Hi5qnxmKKtIiOB5ev3ILtFNhnw71g1C426jPquYenpC9Zg9oTB8A8IwtI1O2WZu6c33n1wwfU7j9G+WR38s/mAxn1E0OvrF4AT/11L8vMSfS8GUEQ6IHqaJo3sA5OMGeSHS7vmdXHl1sMYH0qCSDjv0b4Jhk9aqNFDdenGfWxbOROtGtmry8U50T+sNu0+Lj/ExYexCBiiD5/Y2lhh5JQ/sOfIOXl7275TuH5srQwGEhJAieCkSbfRePDklbx94LgDLuxfifHDemDguLka5z5+7ijrpmJna42fh3bH/OWbsWztLnX50TOXcXL7UllfUW5kZCh7NR4+fYWOA6cgNExZ/+ev32HhtJHfDE5mTxgiAwxRzw+f3NTlc5ZukD+fvHgj6y8CqOPnrso8quSuozbaU/QKiSBoy94TGD/zL1m2YedRGQj+2Ke97L25fPOB+vxC+XPLoOrY2Svy9rZ9J3Fh30pMGdUXF66OlmVPX7yBfZuhiBRRUZTdR87J88R7ZMk/OzTqZJ7NDHXaDZOBu/Dg6SsZ6In3rggKk0r8XsSXAJEDtffofxrHdh0+KwNX0cv48o2TLBO/D/E3cfTsFZlvSKQvHMIj0oGDJy/K4KnhD1WQ2TQTGtWugv3HLsR6bsvGtfDZ1w/nr96BpXlW9UV80Pr5B6BmlTLqc6MHT+JxxXnX7jyCaSYTFC6QW+NxxX1VwZMgPvjvPnyOfLlzJug13Lz3RP1hL4gA5eR/11C3ZsUYQ4Ybdx3TuN28QU0YGChkz0701+Tm4S17VmpFvaZyJQvD2soCG3cdVwcmgkgmFm0SH0uLrDIxf/uBUxrBU0IlRx211Z717SvLn6LnKbq/N+6TPxvWrqJR/tHVQx08CX7+gTJ5u0yJQrC2MpdlIthWBU/i+S2ymcnezVdvPsjzvrb78Fl18CSInqtPrh6yd1VXxO9GBEli6FOlbo2KsLLMhr3R3ttE+sAeKCIdEENFDtfuyZ4PkQRsYGiAw6cvxXpugby5kM0sCx6e2xLrcTFcolK0UF5MGN4TtaqUjTEt3yyL5u2PLh4xHsvb1x8lihZI0Gt4/c45ZtlbZxmsWVkoAw2V9x9cYrwm8aEs8nVio5pJl9tWmQcjApboxJDcOyfNx/xaPjtlIPjspebwV0IlRx211Z6iDuHh4Xjz/qNGubiPt48f7HJZa5S/efcxluf6IH/myZVD3k+Vdyd62vLmyiF7dlS8PiuHOqP7+vXL53n/UT6erogh1FMXbsi/owUrlH8f7ZvXkb1+F6/f19nzEiUEAygiHRHDbgumjZC9F2LILK4p9GLat5iZN2LyoliPe3gp83VEwLR3zVz4+gdgwcotePv+E4JDQmRvgchtEr0p0YXHMVvtG7PMk+TrHBjxmsRsK5FzE9usOZHvom8puY5x5RRFH277Xj8N6CST7cXw3oLlm2UgJtphxvhBMXrE9Gn3obNo3dheJo6L/L3GdavJ4UtttgVRUjCAItIRMYTy+y/D5T/+If/7MjPta2+dPqF2tfK4cfdJvMm4Is9FDFsNGPcbrt1+pC7PY6ebHoCCeXPFLMuXCwGBQfCISsaOy1unj/JDWPSkxNbzouL00VXdGyRyvlREb4h4XSIXKM7n+PBJ/ixWOG+8dYnrgzY56qit9hR1MDQ0lHV46ajMBRKyW5rL2XQfnDWHMPPntY3luezkz/fOyt6tlo1q4eL1exg3Y5nGeSJQF/lIXxPP/bX8eWxlntn3ii8YOnf5lkwqF4njtx88kz12YjiSSN9SztcMojRGfDBO/G0FFq7cglPnr8ebLyU+jEcP7hLjmJi6rhqqE0M4gmpWlmp5hL6dm+uk/mIByjLFv+TCiCnr4tv/+St3vrkWk5j9Joa4xkbbwiM6kW8j3Hv8Un449u7UVL4WFTHbUAQG3xomvXLzIbq2aQS7nJpDWF//HoSvl4RIjjpqqz3PXrwpfw7qoTmlf0gv5e3TDjc0ysUEgujLFmTJnAkdW9aTifCqocLw8AiN95IqqIptCQehY8v6Mu8u+rk5baxwNgETEr5F/I6+/v2oiHruP35BJo6LNhcBqzaCNqLvxR4oIh3adejsN8+5euuhTBoWU9JLFSsoP1DDwsLkN/6Wjewx7ffVOHL6Mm7eewqvz75YOmu0XOpAfGfv2KJejA9BbREfUltXzNCYdi8sXLn1m/cVvWq/L9+MyaP6IE8uGznTSiQyi+UZmtavgS17TsgEaBHAiFlwYqaVWK/q4AkH2avTpU3DGPk+sZn6+yrs/3c+TmxbIpcxEFPfRU5Ow9qV5fR64X5U4rYYrhIz30Tbnjx/PdnqqI32fPz8jVxJvVfHpjLQEDM6y5cuKmfmiZ7O6DPwhFdvnLDo159QvlQRuHl6o2ubhjJ5fMz0pepzRNA1dkg3LJ4xCjfuPUGJwvllsnZcr0nM9hRtLZbaEEseiPwp0XO3de9JfC+RXN+7UzOMGthZPr+752eN3j7xdzSwe2vYVy2H2Us0l+sg0hcGUEQpwMQ5K3D/yUv06tAUk0b0Rlh4uBxqETONxNCeIIKnPj/NxLSxAzBheC94+/rJ4yKZVix3oG0isLt5/6n8kBU9PC9ev8foaUsS/O3/r39349XbDxjcs418DMH5kzsuXLkjZ5+piEBFLNIopuP/MqYfnr58i76jZsmp7QkJLFr2Hi/XcurdqTkyZjDGh49ucvaWilj9XSzx0KtTM9SrWVEOhVVtPkAuaZAcddRWe/4840+8c/oke2Ga1q8ON3dv/Ll2J/74e1uMc1+/+4hf5q+WuXGF8tvJYcqhE36XwbnKn2t2wtTEBG2b/SDXpxLLEvQeOVMGlLERSzqUKJofI/t3RBZTU/m+m/zbSq0sJfDHqu2ws7XBsL4d5JpmIiCMHkCJAEu0eZECuWMsdUCkLwrYVGImHhFpEKs8/7v9MKbMW6XvqqQJbM/vd3L7Enh99kOXIb/ouypEEnOgiIgoRStbsjBKFy8k16IiSik4hEdERClSsUJ5ZfA0pFdbuWinyD8jSinYA0VERCmSmOknktzF7MdhkxYiOCRU31UiUmMOFBEREVEisQeKiIiIKJEYQBERERElEgMoIiIiokRiAEVERESUSAygiIiIiBKJARQRERFRIjGAIiIiIkokBlBEREREicQAioiIiAiJ83/M7uyi102rFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calibration plots: one-vs-rest\n",
    "# ==============================================================================\n",
    "for level, code in {'low': 0, 'medium': 1, 'high': 2}.items():\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(6, 2.5))\n",
    "\n",
    "    y_prob = predictions.loc[:, f'{level}_proba'].rename(code)\n",
    "    y_true = (data.loc[predictions.index, 'encoded_value'] == code).astype(int)\n",
    "    prob_true, prob_pred = calibration_curve(\n",
    "        y_true=y_true,\n",
    "        y_prob=y_prob,\n",
    "        n_bins=10\n",
    "    )\n",
    "\n",
    "    disp = CalibrationDisplay(prob_true=prob_true, prob_pred=prob_pred, y_prob=y_prob)\n",
    "    disp.plot(name=level, ax=ax, color='C' + str(code))\n",
    "\n",
    "    ax.set_title(f\"{level}\")\n",
    "    ax.set_xlabel(\"Mean predicted probability\")\n",
    "    ax.set_ylabel(\"Fraction of positives\")\n",
    "    ax.get_lines()[0].set_color('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c985c07",
   "metadata": {},
   "source": [
    "After applying calibration techniques, the calibration curves are much closer to the diagonal line, indicating that the predicted probabilities are now more aligned with the actual observed frequencies. For instance, for the class 'low', when the classifier predicts a probability of 0.8, the actual frequency of the positive class is now around 0.75, showing a significant improvement compared to before calibration."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "skforecast_py12",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
